Quartile search algorithm:
Quartiles are calculated using the median. If the number of records is an even number, i.e. 2n, then the first quartile (Q1) is equal to the median of n smallest records, and the third quartile (Q3) is equal to the median of n largest records.
If the number of records is odd, that is, in the form (2n + 1), then
Range: is the difference between the largest value and the smallest value in a given dataset.
Interquartile range:
Interquartile range (IQR), also called mean or mean 50% , or technically H-spread — it is the difference between the third quartile (Q3) and the first quartile (Q1). It covers the distribution center and contains 50% of the observations. IQR = Q3 — Q1
Uses :
Make a decision
Suppose that if we have two datasets and their interquartile ranges are IR1 and IR2, and if IR1 & gt; IR2, it is said that the data in IR1 has more variability than the data in IR2, and the data in IR2 is preferable.
Example :
Interquartile range using numpy.median
|
|
Output: 34.0
Interquartile range using numpy.percentile
|
|
Output: 34.0Interquartile range using scipy.stats.iqr
|
|
Output: 34.0
< strong> Quartile Deviation
Quartile Deviation — this is half the difference between the third quartile (Q3) and the first quartile (Q1), i.e. half of the interquartile range (IQR). (Q3 — Q1) / 2 = IQR / 2
Make a decision
Dataset with higher quartile deviation , has higher volatility.
Quartile deflection using numpy.median
# import the numpy library as np import numpy as np data = [ 32 , 36 , 46 , 47 , 56 , 69 75 , 79 , 79 , 88 , 89 , 91 , 92 , 93 , 96 , 97 , 101 , 105 , 112 , 116 ]
# First quartile (Q1) Q1 = np.median (data [: 10 ])
# Third quartile (Q3) Q3 = np.median (data [ 10 :])
# Interquartile range (IQR) IQR = Q3 - Q1
# Quartile Deviation qd = IQR / 2 print (qd)
Output: 17.0
Tried to perform REST GET through python requests with the following code and I got error.
Code snip:
import requests
header = {"Authorization": "Bearer..."}
url = az_base_url + az_subscription_id + "/resourcegroups/Default-Networking/resources?" + az_api_version
r = requests.get(url, headers=header)
Error:
/usr/local/lib/python2.7/dist-packages/requests/packages/urllib3/util/ssl_.py:79:
InsecurePlatformWarning: A true SSLContext object is not available.
This prevents urllib3 from configuring SSL appropriately and may cause certain SSL connections to fail.
For more information, see https://urllib3.readthedocs.org/en/latest/security.html#insecureplatformwarning.
InsecurePlatformWarning
My python version is 2.7.3. I tried to install urllib3 and requests[security] as some other thread suggests, I still got the same error.
Wonder if anyone can provide some tips?
In python, I have to instantiate certain class, knowing its name in a string, but this class "lives" in a dynamically imported module. An example follows:
loader-class script:
import sys
class loader:
def __init__(self, module_name, class_name): # both args are strings
try:
__import__(module_name)
modul = sys.modules[module_name]
instance = modul.class_name() # obviously this doesn"t works, here is my main problem!
except ImportError:
# manage import error
some-dynamically-loaded-module script:
class myName:
# etc...
I use this arrangement to make any dynamically-loaded-module to be used by the loader-class following certain predefined behaviours in the dyn-loaded-modules...
Recently began branching out from my safe place (R) into Python and and am a bit confused by the cell localization/selection in Pandas. I"ve read the documentation but I"m struggling to understand the practical implications of the various localization/selection options.
Is there a reason why I should ever use .loc or .iloc over at, and iat or vice versa? In what situations should I use which method?
Note: future readers be aware that this question is old and was written before pandas v0.20 when there used to exist a function called
.ix. This method was later split into two -locandiloc- to make the explicit distinction between positional and label based indexing. Please beware thatixwas discontinued due to inconsistent behavior and being hard to grok, and no longer exists in current versions of pandas (>= 1.0).
I"m trying to write a simple Python script that will copy a index.tpl to index.html in all of the subdirectories (with a few exceptions).
I"m getting bogged down by trying to get the list of subdirectories.
I want to find mean and standard deviation of 1st, 2nd,... digits of several (Z) lists. For example, I have
A_rank=[0.8,0.4,1.2,3.7,2.6,5.8]
B_rank=[0.1,2.8,3.7,2.6,5,3.4]
C_Rank=[1.2,3.4,0.5,0.1,2.5,6.1]
# etc (up to Z_rank )...
Now I want to take the mean and std of *_Rank[0], the mean and std of *_Rank[1], etc.
(ie: mean and std of the 1st digit from all the (A..Z)_rank lists;
the mean and std of the 2nd digit from all the (A..Z)_rank lists;
the mean and std of the 3rd digit...; etc).
I"m using numpy.linalg.eig to obtain a list of eigenvalues and eigenvectors:
A = someMatrixArray
from numpy.linalg import eig as eigenValuesAndVectors
solution = eigenValuesAndVectors(A)
eigenValues = solution[0]
eigenVectors = solution[1]
I would like to sort my eigenvalues (e.g. from lowest to highest), in a way I know what is the associated eigenvector after the sorting.
I"m not finding any way of doing that with python functions. Is there any simple way or do I have to code my sort version?
I"m making a Python parser, and this is really confusing me:
>>> 1 in [] in "a"
False
>>> (1 in []) in "a"
TypeError: "in <string>" requires string as left operand, not bool
>>> 1 in ([] in "a")
TypeError: "in <string>" requires string as left operand, not list
How exactly does in work in Python, with regards to associativity, etc.?
Why do no two of these expressions behave the same way?
How to calculate probability in normal distribution given mean, std in Python? I can always explicitly code my own function according to the definition like the OP in this question did: Calculating Probability of a Random Variable in a Distribution in Python
Just wondering if there is a library function call will allow you to do this. In my imagine it would like this:
nd = NormalDistribution(mu=100, std=12)
p = nd.prob(98)
There is a similar question in Perl: How can I compute the probability at a point given a normal distribution in Perl?. But I didn"t see one in Python.
Numpy has a random.normal function, but it"s like sampling, not exactly what I want.
The Python 3 range() object doesn"t produce numbers immediately; it is a smart sequence object that produces numbers on demand. All it contains is your start, stop and step values, then as you iterate over the object the next integer is calculated each iteration.
The object also implements the object.__contains__ hook, and calculates if your number is part of its range. Calculating is a (near) constant time operation *. There is never a need to scan through all possible integers in the range.
From the range() object documentation:
The advantage of the
rangetype over a regularlistortupleis that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores thestart,stopandstepvalues, calculating individual items and subranges as needed).
So at a minimum, your range() object would do:
class my_range:
def __init__(self, start, stop=None, step=1, /):
if stop is None:
start, stop = 0, start
self.start, self.stop, self.step = start, stop, step
if step < 0:
lo, hi, step = stop, start, -step
else:
lo, hi = start, stop
self.length = 0 if lo > hi else ((hi - lo - 1) // step) + 1
def __iter__(self):
current = self.start
if self.step < 0:
while current > self.stop:
yield current
current += self.step
else:
while current < self.stop:
yield current
current += self.step
def __len__(self):
return self.length
def __getitem__(self, i):
if i < 0:
i += self.length
if 0 <= i < self.length:
return self.start + i * self.step
raise IndexError("my_range object index out of range")
def __contains__(self, num):
if self.step < 0:
if not (self.stop < num <= self.start):
return False
else:
if not (self.start <= num < self.stop):
return False
return (num - self.start) % self.step == 0
This is still missing several things that a real range() supports (such as the .index() or .count() methods, hashing, equality testing, or slicing), but should give you an idea.
I also simplified the __contains__ implementation to only focus on integer tests; if you give a real range() object a non-integer value (including subclasses of int), a slow scan is initiated to see if there is a match, just as if you use a containment test against a list of all the contained values. This was done to continue to support other numeric types that just happen to support equality testing with integers but are not expected to support integer arithmetic as well. See the original Python issue that implemented the containment test.
* Near constant time because Python integers are unbounded and so math operations also grow in time as N grows, making this a O(log N) operation. Since it’s all executed in optimised C code and Python stores integer values in 30-bit chunks, you’d run out of memory before you saw any performance impact due to the size of the integers involved here.
You have four main options for converting types in pandas:
to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)
astype() - convert (almost) any type to (almost) any other type (even if it"s not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).
infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.
convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.NA (pandas" object to indicate a missing value).
Read on for more detailed explanations and usage of each of these methods.
to_numeric()The best way to convert one or more columns of a DataFrame to numeric values is to use pandas.to_numeric().
This function will try to change non-numeric objects (such as strings) into integers or floating point numbers as appropriate.
The input to to_numeric() is a Series or a single column of a DataFrame.
>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0 8
1 6
2 7.5
3 3
4 0.9
dtype: object
>>> pd.to_numeric(s) # convert everything to float values
0 8.0
1 6.0
2 7.5
3 3.0
4 0.9
dtype: float64
As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:
# convert Series
my_series = pd.to_numeric(my_series)
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])
You can also use it to convert multiple columns of a DataFrame via the apply() method:
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)
As long as your values can all be converted, that"s probably all you need.
But what if some values can"t be converted to a numeric type?
to_numeric() also takes an errors keyword argument that allows you to force non-numeric values to be NaN, or simply ignore columns containing these values.
Here"s an example using a Series of strings s which has the object dtype:
>>> s = pd.Series(["1", "2", "4.7", "pandas", "10"])
>>> s
0 1
1 2
2 4.7
3 pandas
4 10
dtype: object
The default behaviour is to raise if it can"t convert a value. In this case, it can"t cope with the string "pandas":
>>> pd.to_numeric(s) # or pd.to_numeric(s, errors="raise")
ValueError: Unable to parse string
Rather than fail, we might want "pandas" to be considered a missing/bad numeric value. We can coerce invalid values to NaN as follows using the errors keyword argument:
>>> pd.to_numeric(s, errors="coerce")
0 1.0
1 2.0
2 4.7
3 NaN
4 10.0
dtype: float64
The third option for errors is just to ignore the operation if an invalid value is encountered:
>>> pd.to_numeric(s, errors="ignore")
# the original Series is returned untouched
This last option is particularly useful when you want to convert your entire DataFrame, but don"t not know which of our columns can be converted reliably to a numeric type. In that case just write:
df.apply(pd.to_numeric, errors="ignore")
The function will be applied to each column of the DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.
By default, conversion with to_numeric() will give you either a int64 or float64 dtype (or whatever integer width is native to your platform).
That"s usually what you want, but what if you wanted to save some memory and use a more compact dtype, like float32, or int8?
to_numeric() gives you the option to downcast to either "integer", "signed", "unsigned", "float". Here"s an example for a simple series s of integer type:
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
Downcasting to "integer" uses the smallest possible integer that can hold the values:
>>> pd.to_numeric(s, downcast="integer")
0 1
1 2
2 -7
dtype: int8
Downcasting to "float" similarly picks a smaller than normal floating type:
>>> pd.to_numeric(s, downcast="float")
0 1.0
1 2.0
2 -7.0
dtype: float32
astype()The astype() method enables you to be explicit about the dtype you want your DataFrame or Series to have. It"s very versatile in that you can try and go from one type to the any other.
Just pick a type: you can use a NumPy dtype (e.g. np.int16), some Python types (e.g. bool), or pandas-specific types (like the categorical dtype).
Call the method on the object you want to convert and astype() will try and convert it for you:
# convert all DataFrame columns to the int64 dtype
df = df.astype(int)
# convert column "a" to int64 dtype and "b" to complex type
df = df.astype({"a": int, "b": complex})
# convert Series to float16 type
s = s.astype(np.float16)
# convert Series to Python strings
s = s.astype(str)
# convert Series to categorical type - see docs for more details
s = s.astype("category")
Notice I said "try" - if astype() does not know how to convert a value in the Series or DataFrame, it will raise an error. For example if you have a NaN or inf value you"ll get an error trying to convert it to an integer.
As of pandas 0.20.0, this error can be suppressed by passing errors="ignore". Your original object will be return untouched.
astype() is powerful, but it will sometimes convert values "incorrectly". For example:
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
These are small integers, so how about converting to an unsigned 8-bit type to save memory?
>>> s.astype(np.uint8)
0 1
1 2
2 249
dtype: uint8
The conversion worked, but the -7 was wrapped round to become 249 (i.e. 28 - 7)!
Trying to downcast using pd.to_numeric(s, downcast="unsigned") instead could help prevent this error.
infer_objects()Version 0.21.0 of pandas introduced the method infer_objects() for converting columns of a DataFrame that have an object datatype to a more specific type (soft conversions).
For example, here"s a DataFrame with two columns of object type. One holds actual integers and the other holds strings representing integers:
>>> df = pd.DataFrame({"a": [7, 1, 5], "b": ["3","2","1"]}, dtype="object")
>>> df.dtypes
a object
b object
dtype: object
Using infer_objects(), you can change the type of column "a" to int64:
>>> df = df.infer_objects()
>>> df.dtypes
a int64
b object
dtype: object
Column "b" has been left alone since its values were strings, not integers. If you wanted to try and force the conversion of both columns to an integer type, you could use df.astype(int) instead.
convert_dtypes()Version 1.0 and above includes a method convert_dtypes() to convert Series and DataFrame columns to the best possible dtype that supports the pd.NA missing value.
Here "best possible" means the type most suited to hold the values. For example, this a pandas integer type if all of the values are integers (or missing values): an object column of Python integer objects is converted to Int64, a column of NumPy int32 values will become the pandas dtype Int32.
With our object DataFrame df, we get the following result:
>>> df.convert_dtypes().dtypes
a Int64
b string
dtype: object
Since column "a" held integer values, it was converted to the Int64 type (which is capable of holding missing values, unlike int64).
Column "b" contained string objects, so was changed to pandas" string dtype.
By default, this method will infer the type from object values in each column. We can change this by passing infer_objects=False:
>>> df.convert_dtypes(infer_objects=False).dtypes
a object
b string
dtype: object
Now column "a" remained an object column: pandas knows it can be described as an "integer" column (internally it ran infer_dtype) but didn"t infer exactly what dtype of integer it should have so did not convert it. Column "b" was again converted to "string" dtype as it was recognised as holding "string" values.
Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool.
The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks. I"ll summarize below - it ends up being just a few lines of code:
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
results = pool.map(my_function, my_array)
Which is the multithreaded version of:
results = []
for item in my_array:
results.append(my_function(item))
Description
Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.
Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.
Implementation
Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.
multiprocessing.dummy is exactly the same as multiprocessing module, but uses threads instead (an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):
multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.
import urllib2
from multiprocessing.dummy import Pool as ThreadPool
urls = [
"http://www.python.org",
"http://www.python.org/about/",
"http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html",
"http://www.python.org/doc/",
"http://www.python.org/download/",
"http://www.python.org/getit/",
"http://www.python.org/community/",
"https://wiki.python.org/moin/",
]
# Make the Pool of workers
pool = ThreadPool(4)
# Open the URLs in their own threads
# and return the results
results = pool.map(urllib2.urlopen, urls)
# Close the pool and wait for the work to finish
pool.close()
pool.join()
And the timing results:
Single thread: 14.4 seconds
4 Pool: 3.1 seconds
8 Pool: 1.4 seconds
13 Pool: 1.3 seconds
Passing multiple arguments (works like this only in Python 3.3 and later):
To pass multiple arrays:
results = pool.starmap(function, zip(list_a, list_b))
Or to pass a constant and an array:
results = pool.starmap(function, zip(itertools.repeat(constant), list_a))
If you are using an earlier version of Python, you can pass multiple arguments via this workaround).
(Thanks to user136036 for the helpful comment.)
How to iterate over rows in a DataFrame in Pandas?
Iteration in Pandas is an anti-pattern and is something you should only do when you have exhausted every other option. You should not use any function with "iter" in its name for more than a few thousand rows or you will have to get used to a lot of waiting.
Do you want to print a DataFrame? Use DataFrame.to_string().
Do you want to compute something? In that case, search for methods in this order (list modified from here):
for loop)DataFrame.apply(): i)  Reductions that can be performed in Cython, ii) Iteration in Python spaceDataFrame.itertuples() and iteritems()DataFrame.iterrows()iterrows and itertuples (both receiving many votes in answers to this question) should be used in very rare circumstances, such as generating row objects/nametuples for sequential processing, which is really the only thing these functions are useful for.
Appeal to Authority
The documentation page on iteration has a huge red warning box that says:
Iterating through pandas objects is generally slow. In many cases, iterating manually over the rows is not needed [...].
* It"s actually a little more complicated than "don"t". df.iterrows() is the correct answer to this question, but "vectorize your ops" is the better one. I will concede that there are circumstances where iteration cannot be avoided (for example, some operations where the result depends on the value computed for the previous row). However, it takes some familiarity with the library to know when. If you"re not sure whether you need an iterative solution, you probably don"t. PS: To know more about my rationale for writing this answer, skip to the very bottom.
A good number of basic operations and computations are "vectorised" by pandas (either through NumPy, or through Cythonized functions). This includes arithmetic, comparisons, (most) reductions, reshaping (such as pivoting), joins, and groupby operations. Look through the documentation on Essential Basic Functionality to find a suitable vectorised method for your problem.
If none exists, feel free to write your own using custom Cython extensions.
List comprehensions should be your next port of call if 1) there is no vectorized solution available, 2) performance is important, but not important enough to go through the hassle of cythonizing your code, and 3) you"re trying to perform elementwise transformation on your code. There is a good amount of evidence to suggest that list comprehensions are sufficiently fast (and even sometimes faster) for many common Pandas tasks.
The formula is simple,
# Iterating over one column - `f` is some function that processes your data
result = [f(x) for x in df["col"]]
# Iterating over two columns, use `zip`
result = [f(x, y) for x, y in zip(df["col1"], df["col2"])]
# Iterating over multiple columns - same data type
result = [f(row[0], ..., row[n]) for row in df[["col1", ...,"coln"]].to_numpy()]
# Iterating over multiple columns - differing data type
result = [f(row[0], ..., row[n]) for row in zip(df["col1"], ..., df["coln"])]
If you can encapsulate your business logic into a function, you can use a list comprehension that calls it. You can make arbitrarily complex things work through the simplicity and speed of raw Python code.
Caveats
List comprehensions assume that your data is easy to work with - what that means is your data types are consistent and you don"t have NaNs, but this cannot always be guaranteed.
zip(df["A"], df["B"], ...) instead of df[["A", "B"]].to_numpy() as the latter implicitly upcasts data to the most common type. As an example if A is numeric and B is string, to_numpy() will cast the entire array to string, which may not be what you want. Fortunately zipping your columns together is the most straightforward workaround to this.*Your mileage may vary for the reasons outlined in the Caveats section above.
Let"s demonstrate the difference with a simple example of adding two pandas columns A + B. This is a vectorizable operaton, so it will be easy to contrast the performance of the methods discussed above.
Benchmarking code, for your reference. The line at the bottom measures a function written in numpandas, a style of Pandas that mixes heavily with NumPy to squeeze out maximum performance. Writing numpandas code should be avoided unless you know what you"re doing. Stick to the API where you can (i.e., prefer vec over vec_numpy).
I should mention, however, that it isn"t always this cut and dry. Sometimes the answer to "what is the best method for an operation" is "it depends on your data". My advice is to test out different approaches on your data before settling on one.
10 Minutes to pandas, and Essential Basic Functionality - Useful links that introduce you to Pandas and its library of vectorized*/cythonized functions.
Enhancing Performance - A primer from the documentation on enhancing standard Pandas operations
Are for-loops in pandas really bad? When should I care? - a detailed writeup by me on list comprehensions and their suitability for various operations (mainly ones involving non-numeric data)
When should I (not) want to use pandas apply() in my code? - apply is slow (but not as slow as the iter* family. There are, however, situations where one can (or should) consider apply as a serious alternative, especially in some GroupBy operations).
* Pandas string methods are "vectorized" in the sense that they are specified on the series but operate on each element. The underlying mechanisms are still iterative, because string operations are inherently hard to vectorize.
A common trend I notice from new users is to ask questions of the form "How can I iterate over my df to do X?". Showing code that calls iterrows() while doing something inside a for loop. Here is why. A new user to the library who has not been introduced to the concept of vectorization will likely envision the code that solves their problem as iterating over their data to do something. Not knowing how to iterate over a DataFrame, the first thing they do is Google it and end up here, at this question. They then see the accepted answer telling them how to, and they close their eyes and run this code without ever first questioning if iteration is not the right thing to do.
The aim of this answer is to help new users understand that iteration is not necessarily the solution to every problem, and that better, faster and more idiomatic solutions could exist, and that it is worth investing time in exploring them. I"m not trying to start a war of iteration vs. vectorization, but I want new users to be informed when developing solutions to their problems with this library.
In Python, what is the purpose of
__slots__and what are the cases one should avoid this?
The special attribute __slots__ allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:
The space savings is from
__dict__.__dict__ and __weakref__ creation if parent classes deny them and you declare __slots__.Small caveat, you should only declare a particular slot one time in an inheritance tree. For example:
class Base:
__slots__ = "foo", "bar"
class Right(Base):
__slots__ = "baz",
class Wrong(Base):
__slots__ = "foo", "bar", "baz" # redundant foo and bar
Python doesn"t object when you get this wrong (it probably should), problems might not otherwise manifest, but your objects will take up more space than they otherwise should. Python 3.8:
>>> from sys import getsizeof
>>> getsizeof(Right()), getsizeof(Wrong())
(56, 72)
This is because the Base"s slot descriptor has a slot separate from the Wrong"s. This shouldn"t usually come up, but it could:
>>> w = Wrong()
>>> w.foo = "foo"
>>> Base.foo.__get__(w)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: foo
>>> Wrong.foo.__get__(w)
"foo"
The biggest caveat is for multiple inheritance - multiple "parent classes with nonempty slots" cannot be combined.
To accommodate this restriction, follow best practices: Factor out all but one or all parents" abstraction which their concrete class respectively and your new concrete class collectively will inherit from - giving the abstraction(s) empty slots (just like abstract base classes in the standard library).
See section on multiple inheritance below for an example.
To have attributes named in __slots__ to actually be stored in slots instead of a __dict__, a class must inherit from object (automatic in Python 3, but must be explicit in Python 2).
To prevent the creation of a __dict__, you must inherit from object and all classes in the inheritance must declare __slots__ and none of them can have a "__dict__" entry.
There are a lot of details if you wish to keep reading.
__slots__: Faster attribute access.The creator of Python, Guido van Rossum, states that he actually created __slots__ for faster attribute access.
It is trivial to demonstrate measurably significant faster access:
import timeit
class Foo(object): __slots__ = "foo",
class Bar(object): pass
slotted = Foo()
not_slotted = Bar()
def get_set_delete_fn(obj):
def get_set_delete():
obj.foo = "foo"
obj.foo
del obj.foo
return get_set_delete
and
>>> min(timeit.repeat(get_set_delete_fn(slotted)))
0.2846834529991611
>>> min(timeit.repeat(get_set_delete_fn(not_slotted)))
0.3664822799983085
The slotted access is almost 30% faster in Python 3.5 on Ubuntu.
>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342
In Python 2 on Windows I have measured it about 15% faster.
__slots__: Memory SavingsAnother purpose of __slots__ is to reduce the space in memory that each object instance takes up.
My own contribution to the documentation clearly states the reasons behind this:
The space saved over using
__dict__can be significant.
SQLAlchemy attributes a lot of memory savings to __slots__.
To verify this, using the Anaconda distribution of Python 2.7 on Ubuntu Linux, with guppy.hpy (aka heapy) and sys.getsizeof, the size of a class instance without __slots__ declared, and nothing else, is 64 bytes. That does not include the __dict__. Thank you Python for lazy evaluation again, the __dict__ is apparently not called into existence until it is referenced, but classes without data are usually useless. When called into existence, the __dict__ attribute is a minimum of 280 bytes additionally.
In contrast, a class instance with __slots__ declared to be () (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two.
For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for __slots__ and __dict__ (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2 attributes):
Python 2.7 Python 3.6
attrs __slots__ __dict__* __slots__ __dict__* | *(no slots defined)
none 16 56 + 272† 16 56 + 112† | †if __dict__ referenced
one 48 56 + 272 48 56 + 112
two 56 56 + 272 56 56 + 112
six 88 56 + 1040 88 56 + 152
11 128 56 + 1040 128 56 + 240
22 216 56 + 3344 216 56 + 408
43 384 56 + 3344 384 56 + 752
So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__.
Just for completeness of my notes, note that there is a one-time cost per slot in the class"s namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called "members".
>>> Foo.foo
<member "foo" of "Foo" objects>
>>> type(Foo.foo)
<class "member_descriptor">
>>> getsizeof(Foo.foo)
72
__slots__:To deny the creation of a __dict__, you must subclass object. Everything subclasses object in Python 3, but in Python 2 you had to be explicit:
class Base(object):
__slots__ = ()
now:
>>> b = Base()
>>> b.a = "a"
Traceback (most recent call last):
File "<pyshell#38>", line 1, in <module>
b.a = "a"
AttributeError: "Base" object has no attribute "a"
Or subclass another class that defines __slots__
class Child(Base):
__slots__ = ("a",)
and now:
c = Child()
c.a = "a"
but:
>>> c.b = "b"
Traceback (most recent call last):
File "<pyshell#42>", line 1, in <module>
c.b = "b"
AttributeError: "Child" object has no attribute "b"
To allow __dict__ creation while subclassing slotted objects, just add "__dict__" to the __slots__ (note that slots are ordered, and you shouldn"t repeat slots that are already in parent classes):
class SlottedWithDict(Child):
__slots__ = ("__dict__", "b")
swd = SlottedWithDict()
swd.a = "a"
swd.b = "b"
swd.c = "c"
and
>>> swd.__dict__
{"c": "c"}
Or you don"t even need to declare __slots__ in your subclass, and you will still use slots from the parents, but not restrict the creation of a __dict__:
class NoSlots(Child): pass
ns = NoSlots()
ns.a = "a"
ns.b = "b"
And:
>>> ns.__dict__
{"b": "b"}
However, __slots__ may cause problems for multiple inheritance:
class BaseA(object):
__slots__ = ("a",)
class BaseB(object):
__slots__ = ("b",)
Because creating a child class from parents with both non-empty slots fails:
>>> class Child(BaseA, BaseB): __slots__ = ()
Traceback (most recent call last):
File "<pyshell#68>", line 1, in <module>
class Child(BaseA, BaseB): __slots__ = ()
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
If you run into this problem, You could just remove __slots__ from the parents, or if you have control of the parents, give them empty slots, or refactor to abstractions:
from abc import ABC
class AbstractA(ABC):
__slots__ = ()
class BaseA(AbstractA):
__slots__ = ("a",)
class AbstractB(ABC):
__slots__ = ()
class BaseB(AbstractB):
__slots__ = ("b",)
class Child(AbstractA, AbstractB):
__slots__ = ("a", "b")
c = Child() # no problem!
"__dict__" to __slots__ to get dynamic assignment:class Foo(object):
__slots__ = "bar", "baz", "__dict__"
and now:
>>> foo = Foo()
>>> foo.boink = "boink"
So with "__dict__" in slots we lose some of the size benefits with the upside of having dynamic assignment and still having slots for the names we do expect.
When you inherit from an object that isn"t slotted, you get the same sort of semantics when you use __slots__ - names that are in __slots__ point to slotted values, while any other values are put in the instance"s __dict__.
Avoiding __slots__ because you want to be able to add attributes on the fly is actually not a good reason - just add "__dict__" to your __slots__ if this is required.
You can similarly add __weakref__ to __slots__ explicitly if you need that feature.
The namedtuple builtin make immutable instances that are very lightweight (essentially, the size of tuples) but to get the benefits, you need to do it yourself if you subclass them:
from collections import namedtuple
class MyNT(namedtuple("MyNT", "bar baz")):
"""MyNT is an immutable and lightweight object"""
__slots__ = ()
usage:
>>> nt = MyNT("bar", "baz")
>>> nt.bar
"bar"
>>> nt.baz
"baz"
And trying to assign an unexpected attribute raises an AttributeError because we have prevented the creation of __dict__:
>>> nt.quux = "quux"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "MyNT" object has no attribute "quux"
You can allow __dict__ creation by leaving off __slots__ = (), but you can"t use non-empty __slots__ with subtypes of tuple.
Even when non-empty slots are the same for multiple parents, they cannot be used together:
class Foo(object):
__slots__ = "foo", "bar"
class Bar(object):
__slots__ = "foo", "bar" # alas, would work if empty, i.e. ()
>>> class Baz(Foo, Bar): pass
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
Using an empty __slots__ in the parent seems to provide the most flexibility, allowing the child to choose to prevent or allow (by adding "__dict__" to get dynamic assignment, see section above) the creation of a __dict__:
class Foo(object): __slots__ = ()
class Bar(object): __slots__ = ()
class Baz(Foo, Bar): __slots__ = ("foo", "bar")
b = Baz()
b.foo, b.bar = "foo", "bar"
You don"t have to have slots - so if you add them, and remove them later, it shouldn"t cause any problems.
Going out on a limb here: If you"re composing mixins or using abstract base classes, which aren"t intended to be instantiated, an empty __slots__ in those parents seems to be the best way to go in terms of flexibility for subclassers.
To demonstrate, first, let"s create a class with code we"d like to use under multiple inheritance
class AbstractBase:
__slots__ = ()
def __init__(self, a, b):
self.a = a
self.b = b
def __repr__(self):
return f"{type(self).__name__}({repr(self.a)}, {repr(self.b)})"
We could use the above directly by inheriting and declaring the expected slots:
class Foo(AbstractBase):
__slots__ = "a", "b"
But we don"t care about that, that"s trivial single inheritance, we need another class we might also inherit from, maybe with a noisy attribute:
class AbstractBaseC:
__slots__ = ()
@property
def c(self):
print("getting c!")
return self._c
@c.setter
def c(self, arg):
print("setting c!")
self._c = arg
Now if both bases had nonempty slots, we couldn"t do the below. (In fact, if we wanted, we could have given AbstractBase nonempty slots a and b, and left them out of the below declaration - leaving them in would be wrong):
class Concretion(AbstractBase, AbstractBaseC):
__slots__ = "a b _c".split()
And now we have functionality from both via multiple inheritance, and can still deny __dict__ and __weakref__ instantiation:
>>> c = Concretion("a", "b")
>>> c.c = c
setting c!
>>> c.c
getting c!
Concretion("a", "b")
>>> c.d = "d"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "Concretion" object has no attribute "d"
__class__ assignment with another class that doesn"t have them (and you can"t add them) unless the slot layouts are identical. (I am very interested in learning who is doing this and why.)You may be able to tease out further caveats from the rest of the __slots__ documentation (the 3.7 dev docs are the most current), which I have made significant recent contributions to.
The current top answers cite outdated information and are quite hand-wavy and miss the mark in some important ways.
__slots__ when instantiating lots of objects"I quote:
"You would want to use
__slots__if you are going to instantiate a lot (hundreds, thousands) of objects of the same class."
Abstract Base Classes, for example, from the collections module, are not instantiated, yet __slots__ are declared for them.
Why?
If a user wishes to deny __dict__ or __weakref__ creation, those things must not be available in the parent classes.
__slots__ contributes to reusability when creating interfaces or mixins.
It is true that many Python users aren"t writing for reusability, but when you are, having the option to deny unnecessary space usage is valuable.
__slots__ doesn"t break picklingWhen pickling a slotted object, you may find it complains with a misleading TypeError:
>>> pickle.loads(pickle.dumps(f))
TypeError: a class that defines __slots__ without defining __getstate__ cannot be pickled
This is actually incorrect. This message comes from the oldest protocol, which is the default. You can select the latest protocol with the -1 argument. In Python 2.7 this would be 2 (which was introduced in 2.3), and in 3.6 it is 4.
>>> pickle.loads(pickle.dumps(f, -1))
<__main__.Foo object at 0x1129C770>
in Python 2.7:
>>> pickle.loads(pickle.dumps(f, 2))
<__main__.Foo object at 0x1129C770>
in Python 3.6
>>> pickle.loads(pickle.dumps(f, 4))
<__main__.Foo object at 0x1129C770>
So I would keep this in mind, as it is a solved problem.
The first paragraph is half short explanation, half predictive. Here"s the only part that actually answers the question
The proper use of
__slots__is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This saves the overhead of one dict for every object that uses slots
The second half is wishful thinking, and off the mark:
While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.
Python actually does something similar to this, only creating the __dict__ when it is accessed, but creating lots of objects with no data is fairly ridiculous.
The second paragraph oversimplifies and misses actual reasons to avoid __slots__. The below is not a real reason to avoid slots (for actual reasons, see the rest of my answer above.):
They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies.
It then goes on to discuss other ways of accomplishing that perverse goal with Python, not discussing anything to do with __slots__.
The third paragraph is more wishful thinking. Together it is mostly off-the-mark content that the answerer didn"t even author and contributes to ammunition for critics of the site.
Create some normal objects and slotted objects:
>>> class Foo(object): pass
>>> class Bar(object): __slots__ = ()
Instantiate a million of them:
>>> foos = [Foo() for f in xrange(1000000)]
>>> bars = [Bar() for b in xrange(1000000)]
Inspect with guppy.hpy().heap():
>>> guppy.hpy().heap()
Partition of a set of 2028259 objects. Total size = 99763360 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 49 64000000 64 64000000 64 __main__.Foo
1 169 0 16281480 16 80281480 80 list
2 1000000 49 16000000 16 96281480 97 __main__.Bar
3 12284 1 987472 1 97268952 97 str
...
Access the regular objects and their __dict__ and inspect again:
>>> for f in foos:
... f.__dict__
>>> guppy.hpy().heap()
Partition of a set of 3028258 objects. Total size = 379763480 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 33 280000000 74 280000000 74 dict of __main__.Foo
1 1000000 33 64000000 17 344000000 91 __main__.Foo
2 169 0 16281480 4 360281480 95 list
3 1000000 33 16000000 4 376281480 99 __main__.Bar
4 12284 0 987472 0 377268952 99 str
...
This is consistent with the history of Python, from Unifying types and classes in Python 2.2
If you subclass a built-in type, extra space is automatically added to the instances to accomodate
__dict__and__weakrefs__. (The__dict__is not initialized until you use it though, so you shouldn"t worry about the space occupied by an empty dictionary for each instance you create.) If you don"t need this extra space, you can add the phrase "__slots__ = []" to your class.
os.listdir()- list in the current directory
With listdir in os module you get the files and the folders in the current dir
import os
arr = os.listdir()
print(arr)
>>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]
arr = os.listdir("c:\files")
globfrom glob
with glob you can specify a type of file to list like this
import glob
txtfiles = []
for file in glob.glob("*.txt"):
txtfiles.append(file)
glob in a list comprehensionmylist = [f for f in glob.glob("*.txt")]
import os
from os import listdir
from os.path import isfile, join
cwd = os.getcwd()
onlyfiles = [os.path.join(cwd, f) for f in os.listdir(cwd) if
os.path.isfile(os.path.join(cwd, f))]
print(onlyfiles)
["G:\getfilesname\getfilesname.py", "G:\getfilesname\example.txt"]
Getting the full path name with
os.path.abspath
You get the full path in return
import os
files_path = [os.path.abspath(x) for x in os.listdir()]
print(files_path)
["F:\documentiapplications.txt", "F:\documenticollections.txt"]
Walk: going through sub directories
os.walk returns the root, the directories list and the files list, that is why I unpacked them in r, d, f in the for loop; it, then, looks for other files and directories in the subfolders of the root and so on until there are no subfolders.
import os
# Getting the current work directory (cwd)
thisdir = os.getcwd()
# r=root, d=directories, f = files
for r, d, f in os.walk(thisdir):
for file in f:
if file.endswith(".docx"):
print(os.path.join(r, file))
os.listdir(): get files in the current directory (Python 2)
In Python 2, if you want the list of the files in the current directory, you have to give the argument as "." or os.getcwd() in the os.listdir method.
import os
arr = os.listdir(".")
print(arr)
>>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]
To go up in the directory tree
# Method 1
x = os.listdir("..")
# Method 2
x= os.listdir("/")
Get files:
os.listdir()in a particular directory (Python 2 and 3)
import os
arr = os.listdir("F:\python")
print(arr)
>>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]
Get files of a particular subdirectory with
os.listdir()
import os
x = os.listdir("./content")
os.walk(".")- current directory
import os
arr = next(os.walk("."))[2]
print(arr)
>>> ["5bs_Turismo1.pdf", "5bs_Turismo1.pptx", "esperienza.txt"]
next(os.walk("."))andos.path.join("dir", "file")
import os
arr = []
for d,r,f in next(os.walk("F:\_python")):
for file in f:
arr.append(os.path.join(r,file))
for f in arr:
print(files)
>>> F:\_python\dict_class.py
>>> F:\_python\programmi.txt
next(os.walk("F:\")- get the full path - list comprehension
[os.path.join(r,file) for r,d,f in next(os.walk("F:\_python")) for file in f]
>>> ["F:\_python\dict_class.py", "F:\_python\programmi.txt"]
os.walk- get full path - all files in sub dirs**
x = [os.path.join(r,file) for r,d,f in os.walk("F:\_python") for file in f]
print(x)
>>> ["F:\_python\dict.py", "F:\_python\progr.txt", "F:\_python\readl.py"]
os.listdir()- get only txt files
arr_txt = [x for x in os.listdir() if x.endswith(".txt")]
print(arr_txt)
>>> ["work.txt", "3ebooks.txt"]
Using
globto get the full path of the files
If I should need the absolute path of the files:
from path import path
from glob import glob
x = [path(f).abspath() for f in glob("F:\*.txt")]
for f in x:
print(f)
>>> F:acquistionline.txt
>>> F:acquisti_2018.txt
>>> F:ootstrap_jquery_ecc.txt
Using
os.path.isfileto avoid directories in the list
import os.path
listOfFiles = [f for f in os.listdir() if os.path.isfile(f)]
print(listOfFiles)
>>> ["a simple game.py", "data.txt", "decorator.py"]
Using
pathlibfrom Python 3.4
import pathlib
flist = []
for p in pathlib.Path(".").iterdir():
if p.is_file():
print(p)
flist.append(p)
>>> error.PNG
>>> exemaker.bat
>>> guiprova.mp3
>>> setup.py
>>> speak_gui2.py
>>> thumb.PNG
With list comprehension:
flist = [p for p in pathlib.Path(".").iterdir() if p.is_file()]
Alternatively, use pathlib.Path() instead of pathlib.Path(".")
Use glob method in pathlib.Path()
import pathlib
py = pathlib.Path().glob("*.py")
for file in py:
print(file)
>>> stack_overflow_list.py
>>> stack_overflow_list_tkinter.py
Get all and only files with os.walk
import os
x = [i[2] for i in os.walk(".")]
y=[]
for t in x:
for f in t:
y.append(f)
print(y)
>>> ["append_to_list.py", "data.txt", "data1.txt", "data2.txt", "data_180617", "os_walk.py", "READ2.py", "read_data.py", "somma_defaltdic.py", "substitute_words.py", "sum_data.py", "data.txt", "data1.txt", "data_180617"]
Get only files with next and walk in a directory
import os
x = next(os.walk("F://python"))[2]
print(x)
>>> ["calculator.bat","calculator.py"]
Get only directories with next and walk in a directory
import os
next(os.walk("F://python"))[1] # for the current dir use (".")
>>> ["python3","others"]
Get all the subdir names with
walk
for r,d,f in os.walk("F:\_python"):
for dirs in d:
print(dirs)
>>> .vscode
>>> pyexcel
>>> pyschool.py
>>> subtitles
>>> _metaprogramming
>>> .ipynb_checkpoints
os.scandir()from Python 3.5 and greater
import os
x = [f.name for f in os.scandir() if f.is_file()]
print(x)
>>> ["calculator.bat","calculator.py"]
# Another example with scandir (a little variation from docs.python.org)
# This one is more efficient than os.listdir.
# In this case, it shows the files only in the current directory
# where the script is executed.
import os
with os.scandir() as i:
for entry in i:
if entry.is_file():
print(entry.name)
>>> ebookmaker.py
>>> error.PNG
>>> exemaker.bat
>>> guiprova.mp3
>>> setup.py
>>> speakgui4.py
>>> speak_gui2.py
>>> speak_gui3.py
>>> thumb.PNG
Examples:
Ex. 1: How many files are there in the subdirectories?
In this example, we look for the number of files that are included in all the directory and its subdirectories.
import os
def count(dir, counter=0):
"returns number of files in dir and subdirs"
for pack in os.walk(dir):
for f in pack[2]:
counter += 1
return dir + " : " + str(counter) + "files"
print(count("F:\python"))
>>> "F:\python" : 12057 files"
Ex.2: How to copy all files from a directory to another?
A script to make order in your computer finding all files of a type (default: pptx) and copying them in a new folder.
import os
import shutil
from path import path
destination = "F:\file_copied"
# os.makedirs(destination)
def copyfile(dir, filetype="pptx", counter=0):
"Searches for pptx (or other - pptx is the default) files and copies them"
for pack in os.walk(dir):
for f in pack[2]:
if f.endswith(filetype):
fullpath = pack[0] + "\" + f
print(fullpath)
shutil.copy(fullpath, destination)
counter += 1
if counter > 0:
print("-" * 30)
print(" ==> Found in: `" + dir + "` : " + str(counter) + " files
")
for dir in os.listdir():
"searches for folders that starts with `_`"
if dir[0] == "_":
# copyfile(dir, filetype="pdf")
copyfile(dir, filetype="txt")
>>> _compiti18Compito Contabilità 1conti.txt
>>> _compiti18Compito Contabilità 1modula4.txt
>>> _compiti18Compito Contabilità 1moduloa4.txt
>>> ------------------------
>>> ==> Found in: `_compiti18` : 3 files
Ex. 3: How to get all the files in a txt file
In case you want to create a txt file with all the file names:
import os
mylist = ""
with open("filelist.txt", "w", encoding="utf-8") as file:
for eachfile in os.listdir():
mylist += eachfile + "
"
file.write(mylist)
Example: txt with all the files of an hard drive
"""
We are going to save a txt file with all the files in your directory.
We will use the function walk()
"""
import os
# see all the methods of os
# print(*dir(os), sep=", ")
listafile = []
percorso = []
with open("lista_file.txt", "w", encoding="utf-8") as testo:
for root, dirs, files in os.walk("D:\"):
for file in files:
listafile.append(file)
percorso.append(root + "\" + file)
testo.write(file + "
")
listafile.sort()
print("N. of files", len(listafile))
with open("lista_file_ordinata.txt", "w", encoding="utf-8") as testo_ordinato:
for file in listafile:
testo_ordinato.write(file + "
")
with open("percorso.txt", "w", encoding="utf-8") as file_percorso:
for file in percorso:
file_percorso.write(file + "
")
os.system("lista_file.txt")
os.system("lista_file_ordinata.txt")
os.system("percorso.txt")
All the file of C: in one text file
This is a shorter version of the previous code. Change the folder where to start finding the files if you need to start from another position. This code generate a 50 mb on text file on my computer with something less then 500.000 lines with files with the complete path.
import os
with open("file.txt", "w", encoding="utf-8") as filewrite:
for r, d, f in os.walk("C:\"):
for file in f:
filewrite.write(f"{r + file}
")
How to write a file with all paths in a folder of a type
With this function you can create a txt file that will have the name of a type of file that you look for (ex. pngfile.txt) with all the full path of all the files of that type. It can be useful sometimes, I think.
import os
def searchfiles(extension=".ttf", folder="H:\"):
"Create a txt file with all the file of a type"
with open(extension[1:] + "file.txt", "w", encoding="utf-8") as filewrite:
for r, d, f in os.walk(folder):
for file in f:
if file.endswith(extension):
filewrite.write(f"{r + file}
")
# looking for png file (fonts) in the hard disk H:
searchfiles(".png", "H:\")
>>> H:4bs_18Dolphins5.png
>>> H:4bs_18Dolphins6.png
>>> H:4bs_18Dolphins7.png
>>> H:5_18marketing htmlassetsimageslogo2.png
>>> H:7z001.png
>>> H:7z002.png
(New) Find all files and open them with tkinter GUI
I just wanted to add in this 2019 a little app to search for all files in a dir and be able to open them by doubleclicking on the name of the file in the list.
import tkinter as tk
import os
def searchfiles(extension=".txt", folder="H:\"):
"insert all files in the listbox"
for r, d, f in os.walk(folder):
for file in f:
if file.endswith(extension):
lb.insert(0, r + "\" + file)
def open_file():
os.startfile(lb.get(lb.curselection()[0]))
root = tk.Tk()
root.geometry("400x400")
bt = tk.Button(root, text="Search", command=lambda:searchfiles(".png", "H:\"))
bt.pack()
lb = tk.Listbox(root)
lb.pack(fill="both", expand=1)
lb.bind("<Double-Button>", lambda x: open_file())
root.mainloop()
This post aims to give readers a primer on SQL-flavored merging with Pandas, how to use it, and when not to use it.
In particular, here"s what this post will go through:
The basics - types of joins (LEFT, RIGHT, OUTER, INNER)
What this post (and other posts by me on this thread) will not go through:
Note Most examples default to INNER JOIN operations while demonstrating various features, unless otherwise specified.
Furthermore, all the DataFrames here can be copied and replicated so you can play with them. Also, see this post on how to read DataFrames from your clipboard.
Lastly, all visual representation of JOIN operations have been hand-drawn using Google Drawings. Inspiration from here.
merge!np.random.seed(0)
left = pd.DataFrame({"key": ["A", "B", "C", "D"], "value": np.random.randn(4)})
right = pd.DataFrame({"key": ["B", "D", "E", "F"], "value": np.random.randn(4)})
left
key value
0 A 1.764052
1 B 0.400157
2 C 0.978738
3 D 2.240893
right
key value
0 B 1.867558
1 D -0.977278
2 E 0.950088
3 F -0.151357
For the sake of simplicity, the key column has the same name (for now).
An INNER JOIN is represented by
Note This, along with the forthcoming figures all follow this convention:
- blue indicates rows that are present in the merge result
- red indicates rows that are excluded from the result (i.e., removed)
- green indicates missing values that are replaced with
NaNs in the result
To perform an INNER JOIN, call merge on the left DataFrame, specifying the right DataFrame and the join key (at the very least) as arguments.
left.merge(right, on="key")
# Or, if you want to be explicit
# left.merge(right, on="key", how="inner")
key value_x value_y
0 B 0.400157 1.867558
1 D 2.240893 -0.977278
This returns only rows from left and right which share a common key (in this example, "B" and "D).
A LEFT OUTER JOIN, or LEFT JOIN is represented by
This can be performed by specifying how="left".
left.merge(right, on="key", how="left")
key value_x value_y
0 A 1.764052 NaN
1 B 0.400157 1.867558
2 C 0.978738 NaN
3 D 2.240893 -0.977278
Carefully note the placement of NaNs here. If you specify how="left", then only keys from left are used, and missing data from right is replaced by NaN.
And similarly, for a RIGHT OUTER JOIN, or RIGHT JOIN which is...
...specify how="right":
left.merge(right, on="key", how="right")
key value_x value_y
0 B 0.400157 1.867558
1 D 2.240893 -0.977278
2 E NaN 0.950088
3 F NaN -0.151357
Here, keys from right are used, and missing data from left is replaced by NaN.
Finally, for the FULL OUTER JOIN, given by
specify how="outer".
left.merge(right, on="key", how="outer")
key value_x value_y
0 A 1.764052 NaN
1 B 0.400157 1.867558
2 C 0.978738 NaN
3 D 2.240893 -0.977278
4 E NaN 0.950088
5 F NaN -0.151357
This uses the keys from both frames, and NaNs are inserted for missing rows in both.
The documentation summarizes these various merges nicely:
If you need LEFT-Excluding JOINs and RIGHT-Excluding JOINs in two steps.
For LEFT-Excluding JOIN, represented as
Start by performing a LEFT OUTER JOIN and then filtering (excluding!) rows coming from left only,
(left.merge(right, on="key", how="left", indicator=True)
.query("_merge == "left_only"")
.drop("_merge", 1))
key value_x value_y
0 A 1.764052 NaN
2 C 0.978738 NaN
Where,
left.merge(right, on="key", how="left", indicator=True)
key value_x value_y _merge
0 A 1.764052 NaN left_only
1 B 0.400157 1.867558 both
2 C 0.978738 NaN left_only
3 D 2.240893 -0.977278 bothAnd similarly, for a RIGHT-Excluding JOIN,
(left.merge(right, on="key", how="right", indicator=True)
.query("_merge == "right_only"")
.drop("_merge", 1))
key value_x value_y
2 E NaN 0.950088
3 F NaN -0.151357Lastly, if you are required to do a merge that only retains keys from the left or right, but not both (IOW, performing an ANTI-JOIN),
You can do this in similar fashion—
(left.merge(right, on="key", how="outer", indicator=True)
.query("_merge != "both"")
.drop("_merge", 1))
key value_x value_y
0 A 1.764052 NaN
2 C 0.978738 NaN
4 E NaN 0.950088
5 F NaN -0.151357
If the key columns are named differently—for example, left has keyLeft, and right has keyRight instead of key—then you will have to specify left_on and right_on as arguments instead of on:
left2 = left.rename({"key":"keyLeft"}, axis=1)
right2 = right.rename({"key":"keyRight"}, axis=1)
left2
keyLeft value
0 A 1.764052
1 B 0.400157
2 C 0.978738
3 D 2.240893
right2
keyRight value
0 B 1.867558
1 D -0.977278
2 E 0.950088
3 F -0.151357
left2.merge(right2, left_on="keyLeft", right_on="keyRight", how="inner")
keyLeft value_x keyRight value_y
0 B 0.400157 B 1.867558
1 D 2.240893 D -0.977278
When merging on keyLeft from left and keyRight from right, if you only want either of the keyLeft or keyRight (but not both) in the output, you can start by setting the index as a preliminary step.
left3 = left2.set_index("keyLeft")
left3.merge(right2, left_index=True, right_on="keyRight")
value_x keyRight value_y
0 0.400157 B 1.867558
1 2.240893 D -0.977278
Contrast this with the output of the command just before (that is, the output of left2.merge(right2, left_on="keyLeft", right_on="keyRight", how="inner")), you"ll notice keyLeft is missing. You can figure out what column to keep based on which frame"s index is set as the key. This may matter when, say, performing some OUTER JOIN operation.
DataFramesFor example, consider
right3 = right.assign(newcol=np.arange(len(right)))
right3
key value newcol
0 B 1.867558 0
1 D -0.977278 1
2 E 0.950088 2
3 F -0.151357 3
If you are required to merge only "new_val" (without any of the other columns), you can usually just subset columns before merging:
left.merge(right3[["key", "newcol"]], on="key")
key value newcol
0 B 0.400157 0
1 D 2.240893 1
If you"re doing a LEFT OUTER JOIN, a more performant solution would involve map:
# left["newcol"] = left["key"].map(right3.set_index("key")["newcol"]))
left.assign(newcol=left["key"].map(right3.set_index("key")["newcol"]))
key value newcol
0 A 1.764052 NaN
1 B 0.400157 0.0
2 C 0.978738 NaN
3 D 2.240893 1.0
As mentioned, this is similar to, but faster than
left.merge(right3[["key", "newcol"]], on="key", how="left")
key value newcol
0 A 1.764052 NaN
1 B 0.400157 0.0
2 C 0.978738 NaN
3 D 2.240893 1.0
To join on more than one column, specify a list for on (or left_on and right_on, as appropriate).
left.merge(right, on=["key1", "key2"] ...)
Or, in the event the names are different,
left.merge(right, left_on=["lkey1", "lkey2"], right_on=["rkey1", "rkey2"])
merge* operations and functionsMerging a DataFrame with Series on index: See this answer.
Besides merge, DataFrame.update and DataFrame.combine_first are also used in certain cases to update one DataFrame with another.
pd.merge_ordered is a useful function for ordered JOINs.
pd.merge_asof (read: merge_asOf) is useful for approximate joins.
This section only covers the very basics, and is designed to only whet your appetite. For more examples and cases, see the documentation on merge, join, and concat as well as the links to the function specifications.
Jump to other topics in Pandas Merging 101 to continue learning:
*You are here.
reload hacksWithout seeing the source it"s difficult to know the root cause, so I"ll have to speak generally.
UnicodeDecodeError: "ascii" codec can"t decode byte generally happens when you try to convert a Python 2.x str that contains non-ASCII to a Unicode string without specifying the encoding of the original string.
In brief, Unicode strings are an entirely separate type of Python string that does not contain any encoding. They only hold Unicode point codes and therefore can hold any Unicode point from across the entire spectrum. Strings contain encoded text, beit UTF-8, UTF-16, ISO-8895-1, GBK, Big5 etc. Strings are decoded to Unicode and Unicodes are encoded to strings. Files and text data are always transferred in encoded strings.
The Markdown module authors probably use unicode() (where the exception is thrown) as a quality gate to the rest of the code - it will convert ASCII or re-wrap existing Unicodes strings to a new Unicode string. The Markdown authors can"t know the encoding of the incoming string so will rely on you to decode strings to Unicode strings before passing to Markdown.
Unicode strings can be declared in your code using the u prefix to strings. E.g.
>>> my_u = u"my ünicôdé strįng"
>>> type(my_u)
<type "unicode">
Unicode strings may also come from file, databases and network modules. When this happens, you don"t need to worry about the encoding.
Conversion from str to Unicode can happen even when you don"t explicitly call unicode().
The following scenarios cause UnicodeDecodeError exceptions:
# Explicit conversion without encoding
unicode("€")
# New style format string into Unicode string
# Python will try to convert value string to Unicode first
u"The currency is: {}".format("€")
# Old style format string into Unicode string
# Python will try to convert value string to Unicode first
u"The currency is: %s" % "€"
# Append string to Unicode
# Python will try to convert string to Unicode first
u"The currency is: " + "€"
In the following diagram, you can see how the word café has been encoded in either "UTF-8" or "Cp1252" encoding depending on the terminal type. In both examples, caf is just regular ascii. In UTF-8, é is encoded using two bytes. In "Cp1252", é is 0xE9 (which is also happens to be the Unicode point value (it"s no coincidence)). The correct decode() is invoked and conversion to a Python Unicode is successfull:
In this diagram, decode() is called with ascii (which is the same as calling unicode() without an encoding given). As ASCII can"t contain bytes greater than 0x7F, this will throw a UnicodeDecodeError exception:
It"s good practice to form a Unicode sandwich in your code, where you decode all incoming data to Unicode strings, work with Unicodes, then encode to strs on the way out. This saves you from worrying about the encoding of strings in the middle of your code.
If you need to bake non-ASCII into your source code, just create Unicode strings by prefixing the string with a u. E.g.
u"Zürich"
To allow Python to decode your source code, you will need to add an encoding header to match the actual encoding of your file. For example, if your file was encoded as "UTF-8", you would use:
# encoding: utf-8
This is only necessary when you have non-ASCII in your source code.
Usually non-ASCII data is received from a file. The io module provides a TextWrapper that decodes your file on the fly, using a given encoding. You must use the correct encoding for the file - it can"t be easily guessed. For example, for a UTF-8 file:
import io
with io.open("my_utf8_file.txt", "r", encoding="utf-8") as my_file:
my_unicode_string = my_file.read()
my_unicode_string would then be suitable for passing to Markdown. If a UnicodeDecodeError from the read() line, then you"ve probably used the wrong encoding value.
The Python 2.7 CSV module does not support non-ASCII characters üò©. Help is at hand, however, with https://pypi.python.org/pypi/backports.csv.
Use it like above but pass the opened file to it:
from backports import csv
import io
with io.open("my_utf8_file.txt", "r", encoding="utf-8") as my_file:
for row in csv.reader(my_file):
yield row
Most Python database drivers can return data in Unicode, but usually require a little configuration. Always use Unicode strings for SQL queries.
MySQLIn the connection string add:
charset="utf8",
use_unicode=True
E.g.
>>> db = MySQLdb.connect(host="localhost", user="root", passwd="passwd", db="sandbox", use_unicode=True, charset="utf8")
Add:
psycopg2.extensions.register_type(psycopg2.extensions.UNICODE)
psycopg2.extensions.register_type(psycopg2.extensions.UNICODEARRAY)
Web pages can be encoded in just about any encoding. The Content-type header should contain a charset field to hint at the encoding. The content can then be decoded manually against this value. Alternatively, Python-Requests returns Unicodes in response.text.
If you must decode strings manually, you can simply do my_string.decode(encoding), where encoding is the appropriate encoding. Python 2.x supported codecs are given here: Standard Encodings. Again, if you get UnicodeDecodeError then you"ve probably got the wrong encoding.
Work with Unicodes as you would normal strs.
print writes through the stdout stream. Python tries to configure an encoder on stdout so that Unicodes are encoded to the console"s encoding. For example, if a Linux shell"s locale is en_GB.UTF-8, the output will be encoded to UTF-8. On Windows, you will be limited to an 8bit code page.
An incorrectly configured console, such as corrupt locale, can lead to unexpected print errors. PYTHONIOENCODING environment variable can force the encoding for stdout.
Just like input, io.open can be used to transparently convert Unicodes to encoded byte strings.
The same configuration for reading will allow Unicodes to be written directly.
Python 3 is no more Unicode capable than Python 2.x is, however it is slightly less confused on the topic. E.g the regular str is now a Unicode string and the old str is now bytes.
The default encoding is UTF-8, so if you .decode() a byte string without giving an encoding, Python 3 uses UTF-8 encoding. This probably fixes 50% of people"s Unicode problems.
Further, open() operates in text mode by default, so returns decoded str (Unicode ones). The encoding is derived from your locale, which tends to be UTF-8 on Un*x systems or an 8-bit code page, such as windows-1251, on Windows boxes.
sys.setdefaultencoding("utf8")It"s a nasty hack (there"s a reason you have to use reload) that will only mask problems and hinder your migration to Python 3.x. Understand the problem, fix the root cause and enjoy Unicode zen.
See Why should we NOT use sys.setdefaultencoding("utf-8") in a py script? for further details
Clear the cache directory where appropriate for your system
Linux and Unix
~/.cache/pip # and it respects the XDG_CACHE_HOME directory.
OS X
~/Library/Caches/pip
Windows
%LocalAppData%pipCache
With pip 20.1 or later, you can find the full path for your operating system easily by typing this in the command line:
pip cache dir
Example output on my Ubuntu installation:
‚ûú pip3 cache dir
/home/tawanda/.cache/pip
For sanity, you probably want to have all datetimes calculated by your DB server, rather than the application server. Calculating the timestamp in the application can lead to problems because network latency is variable, clients experience slightly different clock drift, and different programming languages occasionally calculate time slightly differently.
SQLAlchemy allows you to do this by passing func.now() or func.current_timestamp() (they are aliases of each other) which tells the DB to calculate the timestamp itself.
server_defaultAdditionally, for a default where you"re already telling the DB to calculate the value, it"s generally better to use server_default instead of default. This tells SQLAlchemy to pass the default value as part of the CREATE TABLE statement.
For example, if you write an ad hoc script against this table, using server_default means you won"t need to worry about manually adding a timestamp call to your script--the database will set it automatically.
onupdate/server_onupdateSQLAlchemy also supports onupdate so that anytime the row is updated it inserts a new timestamp. Again, best to tell the DB to calculate the timestamp itself:
from sqlalchemy.sql import func
time_created = Column(DateTime(timezone=True), server_default=func.now())
time_updated = Column(DateTime(timezone=True), onupdate=func.now())
There is a server_onupdate parameter, but unlike server_default, it doesn"t actually set anything serverside. It just tells SQLalchemy that your database will change the column when an update happens (perhaps you created a trigger on the column ), so SQLAlchemy will ask for the return value so it can update the corresponding object.
You might be surprised to notice that if you make a bunch of changes within a single transaction, they all have the same timestamp. That"s because the SQL standard specifies that CURRENT_TIMESTAMP returns values based on the start of the transaction.
PostgreSQL provides the non-SQL-standard statement_timestamp() and clock_timestamp() which do change within a transaction. Docs here: https://www.postgresql.org/docs/current/static/functions-datetime.html#FUNCTIONS-DATETIME-CURRENT
If you want to use UTC timestamps, a stub of implementation for func.utcnow() is provided in SQLAlchemy documentation. You need to provide appropriate driver-specific functions on your own though.
I have two Python dictionaries, and I want to write a single expression that returns these two dictionaries, merged (i.e. taking the union). The update() method would be what I need, if it returned its result instead of modifying a dictionary in-place.
>>> x = {"a": 1, "b": 2}
>>> y = {"b": 10, "c": 11}
>>> z = x.update(y)
>>> print(z)
None
>>> x
{"a": 1, "b": 10, "c": 11}
How can I get that final merged dictionary in z, not x?
(To be extra-clear, the last-one-wins conflict-handling of dict.update() is what I"m looking for as well.)
How do I access the index in a for loop like the following?
ints = [8, 23, 45, 12, 78]
for i in ints:
print("item #{} = {}".format(???, i))
I want to get this output:
item #1 = 8
item #2 = 23
item #3 = 45
item #4 = 12
item #5 = 78
When I loop through it using a for loop, how do I access the loop index, from 1 to 5 in this case?
I am a bit puzzled by the following code:
d = {"x": 1, "y": 2, "z": 3}
for key in d:
print (key, "corresponds to", d[key])
What I don"t understand is the key portion. How does Python recognize that it needs only to read the key from the dictionary? Is key a special word in Python? Or is it simply a variable?
How can I create or use a global variable in a function?
If I create a global variable in one function, how can I use that global variable in another function? Do I need to store the global variable in a local variable of the function which needs its access?
How can I raise an exception in Python so that it can later be caught via an except block?
What is the best way to go about calling a function given a string with the function"s name in a Python program. For example, let"s say that I have a module foo, and I have a string whose content is "bar". What is the best way to call foo.bar()?
I need to get the return value of the function, which is why I don"t just use eval. I figured out how to do it by using eval to define a temp function that returns the result of that function call, but I"m hoping that there is a more elegant way to do this.
Can someone please explain the exact meaning of having single and double leading underscores before an object"s name in Python, and the difference between both?
Also, does that meaning stay the same regardless of whether the object in question is a variable, a function, a method, etc.?
I am writing a quick-and-dirty script to generate plots on the fly. I am using the code below (from Matplotlib documentation) as a starting point:
from pylab import figure, axes, pie, title, show
# Make a square figure and axes
figure(1, figsize=(6, 6))
ax = axes([0.1, 0.1, 0.8, 0.8])
labels = "Frogs", "Hogs", "Dogs", "Logs"
fracs = [15, 30, 45, 10]
explode = (0, 0.05, 0, 0)
pie(fracs, explode=explode, labels=labels, autopct="%1.1f%%", shadow=True)
title("Raining Hogs and Dogs", bbox={"facecolor": "0.8", "pad": 5})
show() # Actually, don"t show, just save to foo.png
I don"t want to display the plot on a GUI, instead, I want to save the plot to a file (say foo.png), so that, for example, it can be used in batch scripts. How do I do that?
What are the differences between these two code fragments?
Using type():
import types
if type(a) is types.DictType:
do_something()
if type(b) in types.StringTypes:
do_something_else()
Using isinstance():
if isinstance(a, dict):
do_something()
if isinstance(b, str) or isinstance(b, unicode):
do_something_else()
Here is the problem:
I have a requirements.txt file that looks like:
BeautifulSoup==3.2.0
Django==1.3
Fabric==1.2.0
Jinja2==2.5.5
PyYAML==3.09
Pygments==1.4
SQLAlchemy==0.7.1
South==0.7.3
amqplib==0.6.1
anyjson==0.3
...
I have a local archive directory containing all the packages + others.
I have created a new virtualenv with
bin/virtualenv testing
Upon activating it, I tried to install the packages according to requirements.txt from the local archive directory.
source bin/activate
pip install -r /path/to/requirements.txt -f file:///path/to/archive/
I got some output that seems to indicate that the installation is fine:
Downloading/unpacking Fabric==1.2.0 (from -r ../testing/requirements.txt (line 3))
Running setup.py egg_info for package Fabric
warning: no previously-included files matching "*" found under directory "docs/_build"
warning: no files found matching "fabfile.py"
Downloading/unpacking South==0.7.3 (from -r ../testing/requirements.txt (line 8))
Running setup.py egg_info for package South
....
But a later check revealed none of the package is installed properly. I cannot import the package, and none is found in the site-packages directory of my virtualenv. So what went wrong?
The Python 3 range() object doesn"t produce numbers immediately; it is a smart sequence object that produces numbers on demand. All it contains is your start, stop and step values, then as you iterate over the object the next integer is calculated each iteration.
The object also implements the object.__contains__ hook, and calculates if your number is part of its range. Calculating is a (near) constant time operation *. There is never a need to scan through all possible integers in the range.
From the range() object documentation:
The advantage of the
rangetype over a regularlistortupleis that a range object will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores thestart,stopandstepvalues, calculating individual items and subranges as needed).
So at a minimum, your range() object would do:
class my_range:
def __init__(self, start, stop=None, step=1, /):
if stop is None:
start, stop = 0, start
self.start, self.stop, self.step = start, stop, step
if step < 0:
lo, hi, step = stop, start, -step
else:
lo, hi = start, stop
self.length = 0 if lo > hi else ((hi - lo - 1) // step) + 1
def __iter__(self):
current = self.start
if self.step < 0:
while current > self.stop:
yield current
current += self.step
else:
while current < self.stop:
yield current
current += self.step
def __len__(self):
return self.length
def __getitem__(self, i):
if i < 0:
i += self.length
if 0 <= i < self.length:
return self.start + i * self.step
raise IndexError("my_range object index out of range")
def __contains__(self, num):
if self.step < 0:
if not (self.stop < num <= self.start):
return False
else:
if not (self.start <= num < self.stop):
return False
return (num - self.start) % self.step == 0
This is still missing several things that a real range() supports (such as the .index() or .count() methods, hashing, equality testing, or slicing), but should give you an idea.
I also simplified the __contains__ implementation to only focus on integer tests; if you give a real range() object a non-integer value (including subclasses of int), a slow scan is initiated to see if there is a match, just as if you use a containment test against a list of all the contained values. This was done to continue to support other numeric types that just happen to support equality testing with integers but are not expected to support integer arithmetic as well. See the original Python issue that implemented the containment test.
* Near constant time because Python integers are unbounded and so math operations also grow in time as N grows, making this a O(log N) operation. Since it’s all executed in optimised C code and Python stores integer values in 30-bit chunks, you’d run out of memory before you saw any performance impact due to the size of the integers involved here.
This is my personal recommendation for beginners: start by learning virtualenv and pip, tools which work with both Python 2 and 3 and in a variety of situations, and pick up other tools once you start needing them.
virtualenv is a very popular tool that creates isolated Python environments for Python libraries. If you"re not familiar with this tool, I highly recommend learning it, as it is a very useful tool, and I"ll be making comparisons to it for the rest of this answer.It works by installing a bunch of files in a directory (eg: env/), and then modifying the PATH environment variable to prefix it with a custom bin directory (eg: env/bin/). An exact copy of the python or python3 binary is placed in this directory, but Python is programmed to look for libraries relative to its path first, in the environment directory. It"s not part of Python"s standard library, but is officially blessed by the PyPA (Python Packaging Authority). Once activated, you can install packages in the virtual environment using pip.
pyenv is used to isolate Python versions. For example, you may want to test your code against Python 2.7, 3.6, 3.7 and 3.8, so you"ll need a way to switch between them. Once activated, it prefixes the PATH environment variable with ~/.pyenv/shims, where there are special files matching the Python commands (python, pip). These are not copies of the Python-shipped commands; they are special scripts that decide on the fly which version of Python to run based on the PYENV_VERSION environment variable, or the .python-version file, or the ~/.pyenv/version file. pyenv also makes the process of downloading and installing multiple Python versions easier, using the command pyenv install.
pyenv-virtualenv is a plugin for pyenv by the same author as pyenv, to allow you to use pyenv and virtualenv at the same time conveniently. However, if you"re using Python 3.3 or later, pyenv-virtualenv will try to run python -m venv if it is available, instead of virtualenv. You can use virtualenv and pyenv together without pyenv-virtualenv, if you don"t want the convenience features.
virtualenvwrapper is a set of extensions to virtualenv (see docs). It gives you commands like mkvirtualenv, lssitepackages, and especially workon for switching between different virtualenv directories. This tool is especially useful if you want multiple virtualenv directories.
pyenv-virtualenvwrapper is a plugin for pyenv by the same author as pyenv, to conveniently integrate virtualenvwrapper into pyenv.
pipenv aims to combine Pipfile, pip and virtualenv into one command on the command-line. The virtualenv directory typically gets placed in ~/.local/share/virtualenvs/XXX, with XXX being a hash of the path of the project directory. This is different from virtualenv, where the directory is typically in the current working directory. pipenv is meant to be used when developing Python applications (as opposed to libraries). There are alternatives to pipenv, such as poetry, which I won"t list here since this question is only about the packages that are similarly named.
pyvenv (not to be confused with pyenv in the previous section) is a script shipped with Python 3 but deprecated in Python 3.6 as it had problems (not to mention the confusing name). In Python 3.6+, the exact equivalent is python3 -m venv.
venv is a package shipped with Python 3, which you can run using python3 -m venv (although for some reason some distros separate it out into a separate distro package, such as python3-venv on Ubuntu/Debian). It serves the same purpose as virtualenv, but only has a subset of its features (see a comparison here). virtualenv continues to be more popular than venv, especially since the former supports both Python 2 and 3.
You have four main options for converting types in pandas:
to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)
astype() - convert (almost) any type to (almost) any other type (even if it"s not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).
infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.
convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.NA (pandas" object to indicate a missing value).
Read on for more detailed explanations and usage of each of these methods.
to_numeric()The best way to convert one or more columns of a DataFrame to numeric values is to use pandas.to_numeric().
This function will try to change non-numeric objects (such as strings) into integers or floating point numbers as appropriate.
The input to to_numeric() is a Series or a single column of a DataFrame.
>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0 8
1 6
2 7.5
3 3
4 0.9
dtype: object
>>> pd.to_numeric(s) # convert everything to float values
0 8.0
1 6.0
2 7.5
3 3.0
4 0.9
dtype: float64
As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:
# convert Series
my_series = pd.to_numeric(my_series)
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])
You can also use it to convert multiple columns of a DataFrame via the apply() method:
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)
As long as your values can all be converted, that"s probably all you need.
But what if some values can"t be converted to a numeric type?
to_numeric() also takes an errors keyword argument that allows you to force non-numeric values to be NaN, or simply ignore columns containing these values.
Here"s an example using a Series of strings s which has the object dtype:
>>> s = pd.Series(["1", "2", "4.7", "pandas", "10"])
>>> s
0 1
1 2
2 4.7
3 pandas
4 10
dtype: object
The default behaviour is to raise if it can"t convert a value. In this case, it can"t cope with the string "pandas":
>>> pd.to_numeric(s) # or pd.to_numeric(s, errors="raise")
ValueError: Unable to parse string
Rather than fail, we might want "pandas" to be considered a missing/bad numeric value. We can coerce invalid values to NaN as follows using the errors keyword argument:
>>> pd.to_numeric(s, errors="coerce")
0 1.0
1 2.0
2 4.7
3 NaN
4 10.0
dtype: float64
The third option for errors is just to ignore the operation if an invalid value is encountered:
>>> pd.to_numeric(s, errors="ignore")
# the original Series is returned untouched
This last option is particularly useful when you want to convert your entire DataFrame, but don"t not know which of our columns can be converted reliably to a numeric type. In that case just write:
df.apply(pd.to_numeric, errors="ignore")
The function will be applied to each column of the DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.
By default, conversion with to_numeric() will give you either a int64 or float64 dtype (or whatever integer width is native to your platform).
That"s usually what you want, but what if you wanted to save some memory and use a more compact dtype, like float32, or int8?
to_numeric() gives you the option to downcast to either "integer", "signed", "unsigned", "float". Here"s an example for a simple series s of integer type:
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
Downcasting to "integer" uses the smallest possible integer that can hold the values:
>>> pd.to_numeric(s, downcast="integer")
0 1
1 2
2 -7
dtype: int8
Downcasting to "float" similarly picks a smaller than normal floating type:
>>> pd.to_numeric(s, downcast="float")
0 1.0
1 2.0
2 -7.0
dtype: float32
astype()The astype() method enables you to be explicit about the dtype you want your DataFrame or Series to have. It"s very versatile in that you can try and go from one type to the any other.
Just pick a type: you can use a NumPy dtype (e.g. np.int16), some Python types (e.g. bool), or pandas-specific types (like the categorical dtype).
Call the method on the object you want to convert and astype() will try and convert it for you:
# convert all DataFrame columns to the int64 dtype
df = df.astype(int)
# convert column "a" to int64 dtype and "b" to complex type
df = df.astype({"a": int, "b": complex})
# convert Series to float16 type
s = s.astype(np.float16)
# convert Series to Python strings
s = s.astype(str)
# convert Series to categorical type - see docs for more details
s = s.astype("category")
Notice I said "try" - if astype() does not know how to convert a value in the Series or DataFrame, it will raise an error. For example if you have a NaN or inf value you"ll get an error trying to convert it to an integer.
As of pandas 0.20.0, this error can be suppressed by passing errors="ignore". Your original object will be return untouched.
astype() is powerful, but it will sometimes convert values "incorrectly". For example:
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
These are small integers, so how about converting to an unsigned 8-bit type to save memory?
>>> s.astype(np.uint8)
0 1
1 2
2 249
dtype: uint8
The conversion worked, but the -7 was wrapped round to become 249 (i.e. 28 - 7)!
Trying to downcast using pd.to_numeric(s, downcast="unsigned") instead could help prevent this error.
infer_objects()Version 0.21.0 of pandas introduced the method infer_objects() for converting columns of a DataFrame that have an object datatype to a more specific type (soft conversions).
For example, here"s a DataFrame with two columns of object type. One holds actual integers and the other holds strings representing integers:
>>> df = pd.DataFrame({"a": [7, 1, 5], "b": ["3","2","1"]}, dtype="object")
>>> df.dtypes
a object
b object
dtype: object
Using infer_objects(), you can change the type of column "a" to int64:
>>> df = df.infer_objects()
>>> df.dtypes
a int64
b object
dtype: object
Column "b" has been left alone since its values were strings, not integers. If you wanted to try and force the conversion of both columns to an integer type, you could use df.astype(int) instead.
convert_dtypes()Version 1.0 and above includes a method convert_dtypes() to convert Series and DataFrame columns to the best possible dtype that supports the pd.NA missing value.
Here "best possible" means the type most suited to hold the values. For example, this a pandas integer type if all of the values are integers (or missing values): an object column of Python integer objects is converted to Int64, a column of NumPy int32 values will become the pandas dtype Int32.
With our object DataFrame df, we get the following result:
>>> df.convert_dtypes().dtypes
a Int64
b string
dtype: object
Since column "a" held integer values, it was converted to the Int64 type (which is capable of holding missing values, unlike int64).
Column "b" contained string objects, so was changed to pandas" string dtype.
By default, this method will infer the type from object values in each column. We can change this by passing infer_objects=False:
>>> df.convert_dtypes(infer_objects=False).dtypes
a object
b string
dtype: object
Now column "a" remained an object column: pandas knows it can be described as an "integer" column (internally it ran infer_dtype) but didn"t infer exactly what dtype of integer it should have so did not convert it. Column "b" was again converted to "string" dtype as it was recognised as holding "string" values.
Since this question was asked in 2010, there has been real simplification in how to do simple multithreading with Python with map and pool.
The code below comes from an article/blog post that you should definitely check out (no affiliation) - Parallelism in one line: A Better Model for Day to Day Threading Tasks. I"ll summarize below - it ends up being just a few lines of code:
from multiprocessing.dummy import Pool as ThreadPool
pool = ThreadPool(4)
results = pool.map(my_function, my_array)
Which is the multithreaded version of:
results = []
for item in my_array:
results.append(my_function(item))
Description
Map is a cool little function, and the key to easily injecting parallelism into your Python code. For those unfamiliar, map is something lifted from functional languages like Lisp. It is a function which maps another function over a sequence.
Map handles the iteration over the sequence for us, applies the function, and stores all of the results in a handy list at the end.
Implementation
Parallel versions of the map function are provided by two libraries:multiprocessing, and also its little known, but equally fantastic step child:multiprocessing.dummy.
multiprocessing.dummy is exactly the same as multiprocessing module, but uses threads instead (an important distinction - use multiple processes for CPU-intensive tasks; threads for (and during) I/O):
multiprocessing.dummy replicates the API of multiprocessing, but is no more than a wrapper around the threading module.
import urllib2
from multiprocessing.dummy import Pool as ThreadPool
urls = [
"http://www.python.org",
"http://www.python.org/about/",
"http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html",
"http://www.python.org/doc/",
"http://www.python.org/download/",
"http://www.python.org/getit/",
"http://www.python.org/community/",
"https://wiki.python.org/moin/",
]
# Make the Pool of workers
pool = ThreadPool(4)
# Open the URLs in their own threads
# and return the results
results = pool.map(urllib2.urlopen, urls)
# Close the pool and wait for the work to finish
pool.close()
pool.join()
And the timing results:
Single thread: 14.4 seconds
4 Pool: 3.1 seconds
8 Pool: 1.4 seconds
13 Pool: 1.3 seconds
Passing multiple arguments (works like this only in Python 3.3 and later):
To pass multiple arrays:
results = pool.starmap(function, zip(list_a, list_b))
Or to pass a constant and an array:
results = pool.starmap(function, zip(itertools.repeat(constant), list_a))
If you are using an earlier version of Python, you can pass multiple arguments via this workaround).
(Thanks to user136036 for the helpful comment.)
How to iterate over rows in a DataFrame in Pandas?
Iteration in Pandas is an anti-pattern and is something you should only do when you have exhausted every other option. You should not use any function with "iter" in its name for more than a few thousand rows or you will have to get used to a lot of waiting.
Do you want to print a DataFrame? Use DataFrame.to_string().
Do you want to compute something? In that case, search for methods in this order (list modified from here):
for loop)DataFrame.apply(): i)  Reductions that can be performed in Cython, ii) Iteration in Python spaceDataFrame.itertuples() and iteritems()DataFrame.iterrows()iterrows and itertuples (both receiving many votes in answers to this question) should be used in very rare circumstances, such as generating row objects/nametuples for sequential processing, which is really the only thing these functions are useful for.
Appeal to Authority
The documentation page on iteration has a huge red warning box that says:
Iterating through pandas objects is generally slow. In many cases, iterating manually over the rows is not needed [...].
* It"s actually a little more complicated than "don"t". df.iterrows() is the correct answer to this question, but "vectorize your ops" is the better one. I will concede that there are circumstances where iteration cannot be avoided (for example, some operations where the result depends on the value computed for the previous row). However, it takes some familiarity with the library to know when. If you"re not sure whether you need an iterative solution, you probably don"t. PS: To know more about my rationale for writing this answer, skip to the very bottom.
A good number of basic operations and computations are "vectorised" by pandas (either through NumPy, or through Cythonized functions). This includes arithmetic, comparisons, (most) reductions, reshaping (such as pivoting), joins, and groupby operations. Look through the documentation on Essential Basic Functionality to find a suitable vectorised method for your problem.
If none exists, feel free to write your own using custom Cython extensions.
List comprehensions should be your next port of call if 1) there is no vectorized solution available, 2) performance is important, but not important enough to go through the hassle of cythonizing your code, and 3) you"re trying to perform elementwise transformation on your code. There is a good amount of evidence to suggest that list comprehensions are sufficiently fast (and even sometimes faster) for many common Pandas tasks.
The formula is simple,
# Iterating over one column - `f` is some function that processes your data
result = [f(x) for x in df["col"]]
# Iterating over two columns, use `zip`
result = [f(x, y) for x, y in zip(df["col1"], df["col2"])]
# Iterating over multiple columns - same data type
result = [f(row[0], ..., row[n]) for row in df[["col1", ...,"coln"]].to_numpy()]
# Iterating over multiple columns - differing data type
result = [f(row[0], ..., row[n]) for row in zip(df["col1"], ..., df["coln"])]
If you can encapsulate your business logic into a function, you can use a list comprehension that calls it. You can make arbitrarily complex things work through the simplicity and speed of raw Python code.
Caveats
List comprehensions assume that your data is easy to work with - what that means is your data types are consistent and you don"t have NaNs, but this cannot always be guaranteed.
zip(df["A"], df["B"], ...) instead of df[["A", "B"]].to_numpy() as the latter implicitly upcasts data to the most common type. As an example if A is numeric and B is string, to_numpy() will cast the entire array to string, which may not be what you want. Fortunately zipping your columns together is the most straightforward workaround to this.*Your mileage may vary for the reasons outlined in the Caveats section above.
Let"s demonstrate the difference with a simple example of adding two pandas columns A + B. This is a vectorizable operaton, so it will be easy to contrast the performance of the methods discussed above.
Benchmarking code, for your reference. The line at the bottom measures a function written in numpandas, a style of Pandas that mixes heavily with NumPy to squeeze out maximum performance. Writing numpandas code should be avoided unless you know what you"re doing. Stick to the API where you can (i.e., prefer vec over vec_numpy).
I should mention, however, that it isn"t always this cut and dry. Sometimes the answer to "what is the best method for an operation" is "it depends on your data". My advice is to test out different approaches on your data before settling on one.
10 Minutes to pandas, and Essential Basic Functionality - Useful links that introduce you to Pandas and its library of vectorized*/cythonized functions.
Enhancing Performance - A primer from the documentation on enhancing standard Pandas operations
Are for-loops in pandas really bad? When should I care? - a detailed writeup by me on list comprehensions and their suitability for various operations (mainly ones involving non-numeric data)
When should I (not) want to use pandas apply() in my code? - apply is slow (but not as slow as the iter* family. There are, however, situations where one can (or should) consider apply as a serious alternative, especially in some GroupBy operations).
* Pandas string methods are "vectorized" in the sense that they are specified on the series but operate on each element. The underlying mechanisms are still iterative, because string operations are inherently hard to vectorize.
A common trend I notice from new users is to ask questions of the form "How can I iterate over my df to do X?". Showing code that calls iterrows() while doing something inside a for loop. Here is why. A new user to the library who has not been introduced to the concept of vectorization will likely envision the code that solves their problem as iterating over their data to do something. Not knowing how to iterate over a DataFrame, the first thing they do is Google it and end up here, at this question. They then see the accepted answer telling them how to, and they close their eyes and run this code without ever first questioning if iteration is not the right thing to do.
The aim of this answer is to help new users understand that iteration is not necessarily the solution to every problem, and that better, faster and more idiomatic solutions could exist, and that it is worth investing time in exploring them. I"m not trying to start a war of iteration vs. vectorization, but I want new users to be informed when developing solutions to their problems with this library.
In Python, what is the purpose of
__slots__and what are the cases one should avoid this?
The special attribute __slots__ allows you to explicitly state which instance attributes you expect your object instances to have, with the expected results:
The space savings is from
__dict__.__dict__ and __weakref__ creation if parent classes deny them and you declare __slots__.Small caveat, you should only declare a particular slot one time in an inheritance tree. For example:
class Base:
__slots__ = "foo", "bar"
class Right(Base):
__slots__ = "baz",
class Wrong(Base):
__slots__ = "foo", "bar", "baz" # redundant foo and bar
Python doesn"t object when you get this wrong (it probably should), problems might not otherwise manifest, but your objects will take up more space than they otherwise should. Python 3.8:
>>> from sys import getsizeof
>>> getsizeof(Right()), getsizeof(Wrong())
(56, 72)
This is because the Base"s slot descriptor has a slot separate from the Wrong"s. This shouldn"t usually come up, but it could:
>>> w = Wrong()
>>> w.foo = "foo"
>>> Base.foo.__get__(w)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: foo
>>> Wrong.foo.__get__(w)
"foo"
The biggest caveat is for multiple inheritance - multiple "parent classes with nonempty slots" cannot be combined.
To accommodate this restriction, follow best practices: Factor out all but one or all parents" abstraction which their concrete class respectively and your new concrete class collectively will inherit from - giving the abstraction(s) empty slots (just like abstract base classes in the standard library).
See section on multiple inheritance below for an example.
To have attributes named in __slots__ to actually be stored in slots instead of a __dict__, a class must inherit from object (automatic in Python 3, but must be explicit in Python 2).
To prevent the creation of a __dict__, you must inherit from object and all classes in the inheritance must declare __slots__ and none of them can have a "__dict__" entry.
There are a lot of details if you wish to keep reading.
__slots__: Faster attribute access.The creator of Python, Guido van Rossum, states that he actually created __slots__ for faster attribute access.
It is trivial to demonstrate measurably significant faster access:
import timeit
class Foo(object): __slots__ = "foo",
class Bar(object): pass
slotted = Foo()
not_slotted = Bar()
def get_set_delete_fn(obj):
def get_set_delete():
obj.foo = "foo"
obj.foo
del obj.foo
return get_set_delete
and
>>> min(timeit.repeat(get_set_delete_fn(slotted)))
0.2846834529991611
>>> min(timeit.repeat(get_set_delete_fn(not_slotted)))
0.3664822799983085
The slotted access is almost 30% faster in Python 3.5 on Ubuntu.
>>> 0.3664822799983085 / 0.2846834529991611
1.2873325658284342
In Python 2 on Windows I have measured it about 15% faster.
__slots__: Memory SavingsAnother purpose of __slots__ is to reduce the space in memory that each object instance takes up.
My own contribution to the documentation clearly states the reasons behind this:
The space saved over using
__dict__can be significant.
SQLAlchemy attributes a lot of memory savings to __slots__.
To verify this, using the Anaconda distribution of Python 2.7 on Ubuntu Linux, with guppy.hpy (aka heapy) and sys.getsizeof, the size of a class instance without __slots__ declared, and nothing else, is 64 bytes. That does not include the __dict__. Thank you Python for lazy evaluation again, the __dict__ is apparently not called into existence until it is referenced, but classes without data are usually useless. When called into existence, the __dict__ attribute is a minimum of 280 bytes additionally.
In contrast, a class instance with __slots__ declared to be () (no data) is only 16 bytes, and 56 total bytes with one item in slots, 64 with two.
For 64 bit Python, I illustrate the memory consumption in bytes in Python 2.7 and 3.6, for __slots__ and __dict__ (no slots defined) for each point where the dict grows in 3.6 (except for 0, 1, and 2 attributes):
Python 2.7 Python 3.6
attrs __slots__ __dict__* __slots__ __dict__* | *(no slots defined)
none 16 56 + 272† 16 56 + 112† | †if __dict__ referenced
one 48 56 + 272 48 56 + 112
two 56 56 + 272 56 56 + 112
six 88 56 + 1040 88 56 + 152
11 128 56 + 1040 128 56 + 240
22 216 56 + 3344 216 56 + 408
43 384 56 + 3344 384 56 + 752
So, in spite of smaller dicts in Python 3, we see how nicely __slots__ scale for instances to save us memory, and that is a major reason you would want to use __slots__.
Just for completeness of my notes, note that there is a one-time cost per slot in the class"s namespace of 64 bytes in Python 2, and 72 bytes in Python 3, because slots use data descriptors like properties, called "members".
>>> Foo.foo
<member "foo" of "Foo" objects>
>>> type(Foo.foo)
<class "member_descriptor">
>>> getsizeof(Foo.foo)
72
__slots__:To deny the creation of a __dict__, you must subclass object. Everything subclasses object in Python 3, but in Python 2 you had to be explicit:
class Base(object):
__slots__ = ()
now:
>>> b = Base()
>>> b.a = "a"
Traceback (most recent call last):
File "<pyshell#38>", line 1, in <module>
b.a = "a"
AttributeError: "Base" object has no attribute "a"
Or subclass another class that defines __slots__
class Child(Base):
__slots__ = ("a",)
and now:
c = Child()
c.a = "a"
but:
>>> c.b = "b"
Traceback (most recent call last):
File "<pyshell#42>", line 1, in <module>
c.b = "b"
AttributeError: "Child" object has no attribute "b"
To allow __dict__ creation while subclassing slotted objects, just add "__dict__" to the __slots__ (note that slots are ordered, and you shouldn"t repeat slots that are already in parent classes):
class SlottedWithDict(Child):
__slots__ = ("__dict__", "b")
swd = SlottedWithDict()
swd.a = "a"
swd.b = "b"
swd.c = "c"
and
>>> swd.__dict__
{"c": "c"}
Or you don"t even need to declare __slots__ in your subclass, and you will still use slots from the parents, but not restrict the creation of a __dict__:
class NoSlots(Child): pass
ns = NoSlots()
ns.a = "a"
ns.b = "b"
And:
>>> ns.__dict__
{"b": "b"}
However, __slots__ may cause problems for multiple inheritance:
class BaseA(object):
__slots__ = ("a",)
class BaseB(object):
__slots__ = ("b",)
Because creating a child class from parents with both non-empty slots fails:
>>> class Child(BaseA, BaseB): __slots__ = ()
Traceback (most recent call last):
File "<pyshell#68>", line 1, in <module>
class Child(BaseA, BaseB): __slots__ = ()
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
If you run into this problem, You could just remove __slots__ from the parents, or if you have control of the parents, give them empty slots, or refactor to abstractions:
from abc import ABC
class AbstractA(ABC):
__slots__ = ()
class BaseA(AbstractA):
__slots__ = ("a",)
class AbstractB(ABC):
__slots__ = ()
class BaseB(AbstractB):
__slots__ = ("b",)
class Child(AbstractA, AbstractB):
__slots__ = ("a", "b")
c = Child() # no problem!
"__dict__" to __slots__ to get dynamic assignment:class Foo(object):
__slots__ = "bar", "baz", "__dict__"
and now:
>>> foo = Foo()
>>> foo.boink = "boink"
So with "__dict__" in slots we lose some of the size benefits with the upside of having dynamic assignment and still having slots for the names we do expect.
When you inherit from an object that isn"t slotted, you get the same sort of semantics when you use __slots__ - names that are in __slots__ point to slotted values, while any other values are put in the instance"s __dict__.
Avoiding __slots__ because you want to be able to add attributes on the fly is actually not a good reason - just add "__dict__" to your __slots__ if this is required.
You can similarly add __weakref__ to __slots__ explicitly if you need that feature.
The namedtuple builtin make immutable instances that are very lightweight (essentially, the size of tuples) but to get the benefits, you need to do it yourself if you subclass them:
from collections import namedtuple
class MyNT(namedtuple("MyNT", "bar baz")):
"""MyNT is an immutable and lightweight object"""
__slots__ = ()
usage:
>>> nt = MyNT("bar", "baz")
>>> nt.bar
"bar"
>>> nt.baz
"baz"
And trying to assign an unexpected attribute raises an AttributeError because we have prevented the creation of __dict__:
>>> nt.quux = "quux"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "MyNT" object has no attribute "quux"
You can allow __dict__ creation by leaving off __slots__ = (), but you can"t use non-empty __slots__ with subtypes of tuple.
Even when non-empty slots are the same for multiple parents, they cannot be used together:
class Foo(object):
__slots__ = "foo", "bar"
class Bar(object):
__slots__ = "foo", "bar" # alas, would work if empty, i.e. ()
>>> class Baz(Foo, Bar): pass
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Error when calling the metaclass bases
multiple bases have instance lay-out conflict
Using an empty __slots__ in the parent seems to provide the most flexibility, allowing the child to choose to prevent or allow (by adding "__dict__" to get dynamic assignment, see section above) the creation of a __dict__:
class Foo(object): __slots__ = ()
class Bar(object): __slots__ = ()
class Baz(Foo, Bar): __slots__ = ("foo", "bar")
b = Baz()
b.foo, b.bar = "foo", "bar"
You don"t have to have slots - so if you add them, and remove them later, it shouldn"t cause any problems.
Going out on a limb here: If you"re composing mixins or using abstract base classes, which aren"t intended to be instantiated, an empty __slots__ in those parents seems to be the best way to go in terms of flexibility for subclassers.
To demonstrate, first, let"s create a class with code we"d like to use under multiple inheritance
class AbstractBase:
__slots__ = ()
def __init__(self, a, b):
self.a = a
self.b = b
def __repr__(self):
return f"{type(self).__name__}({repr(self.a)}, {repr(self.b)})"
We could use the above directly by inheriting and declaring the expected slots:
class Foo(AbstractBase):
__slots__ = "a", "b"
But we don"t care about that, that"s trivial single inheritance, we need another class we might also inherit from, maybe with a noisy attribute:
class AbstractBaseC:
__slots__ = ()
@property
def c(self):
print("getting c!")
return self._c
@c.setter
def c(self, arg):
print("setting c!")
self._c = arg
Now if both bases had nonempty slots, we couldn"t do the below. (In fact, if we wanted, we could have given AbstractBase nonempty slots a and b, and left them out of the below declaration - leaving them in would be wrong):
class Concretion(AbstractBase, AbstractBaseC):
__slots__ = "a b _c".split()
And now we have functionality from both via multiple inheritance, and can still deny __dict__ and __weakref__ instantiation:
>>> c = Concretion("a", "b")
>>> c.c = c
setting c!
>>> c.c
getting c!
Concretion("a", "b")
>>> c.d = "d"
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: "Concretion" object has no attribute "d"
__class__ assignment with another class that doesn"t have them (and you can"t add them) unless the slot layouts are identical. (I am very interested in learning who is doing this and why.)You may be able to tease out further caveats from the rest of the __slots__ documentation (the 3.7 dev docs are the most current), which I have made significant recent contributions to.
The current top answers cite outdated information and are quite hand-wavy and miss the mark in some important ways.
__slots__ when instantiating lots of objects"I quote:
"You would want to use
__slots__if you are going to instantiate a lot (hundreds, thousands) of objects of the same class."
Abstract Base Classes, for example, from the collections module, are not instantiated, yet __slots__ are declared for them.
Why?
If a user wishes to deny __dict__ or __weakref__ creation, those things must not be available in the parent classes.
__slots__ contributes to reusability when creating interfaces or mixins.
It is true that many Python users aren"t writing for reusability, but when you are, having the option to deny unnecessary space usage is valuable.
__slots__ doesn"t break picklingWhen pickling a slotted object, you may find it complains with a misleading TypeError:
>>> pickle.loads(pickle.dumps(f))
TypeError: a class that defines __slots__ without defining __getstate__ cannot be pickled
This is actually incorrect. This message comes from the oldest protocol, which is the default. You can select the latest protocol with the -1 argument. In Python 2.7 this would be 2 (which was introduced in 2.3), and in 3.6 it is 4.
>>> pickle.loads(pickle.dumps(f, -1))
<__main__.Foo object at 0x1129C770>
in Python 2.7:
>>> pickle.loads(pickle.dumps(f, 2))
<__main__.Foo object at 0x1129C770>
in Python 3.6
>>> pickle.loads(pickle.dumps(f, 4))
<__main__.Foo object at 0x1129C770>
So I would keep this in mind, as it is a solved problem.
The first paragraph is half short explanation, half predictive. Here"s the only part that actually answers the question
The proper use of
__slots__is to save space in objects. Instead of having a dynamic dict that allows adding attributes to objects at anytime, there is a static structure which does not allow additions after creation. This saves the overhead of one dict for every object that uses slots
The second half is wishful thinking, and off the mark:
While this is sometimes a useful optimization, it would be completely unnecessary if the Python interpreter was dynamic enough so that it would only require the dict when there actually were additions to the object.
Python actually does something similar to this, only creating the __dict__ when it is accessed, but creating lots of objects with no data is fairly ridiculous.
The second paragraph oversimplifies and misses actual reasons to avoid __slots__. The below is not a real reason to avoid slots (for actual reasons, see the rest of my answer above.):
They change the behavior of the objects that have slots in a way that can be abused by control freaks and static typing weenies.
It then goes on to discuss other ways of accomplishing that perverse goal with Python, not discussing anything to do with __slots__.
The third paragraph is more wishful thinking. Together it is mostly off-the-mark content that the answerer didn"t even author and contributes to ammunition for critics of the site.
Create some normal objects and slotted objects:
>>> class Foo(object): pass
>>> class Bar(object): __slots__ = ()
Instantiate a million of them:
>>> foos = [Foo() for f in xrange(1000000)]
>>> bars = [Bar() for b in xrange(1000000)]
Inspect with guppy.hpy().heap():
>>> guppy.hpy().heap()
Partition of a set of 2028259 objects. Total size = 99763360 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 49 64000000 64 64000000 64 __main__.Foo
1 169 0 16281480 16 80281480 80 list
2 1000000 49 16000000 16 96281480 97 __main__.Bar
3 12284 1 987472 1 97268952 97 str
...
Access the regular objects and their __dict__ and inspect again:
>>> for f in foos:
... f.__dict__
>>> guppy.hpy().heap()
Partition of a set of 3028258 objects. Total size = 379763480 bytes.
Index Count % Size % Cumulative % Kind (class / dict of class)
0 1000000 33 280000000 74 280000000 74 dict of __main__.Foo
1 1000000 33 64000000 17 344000000 91 __main__.Foo
2 169 0 16281480 4 360281480 95 list
3 1000000 33 16000000 4 376281480 99 __main__.Bar
4 12284 0 987472 0 377268952 99 str
...
This is consistent with the history of Python, from Unifying types and classes in Python 2.2
If you subclass a built-in type, extra space is automatically added to the instances to accomodate
__dict__and__weakrefs__. (The__dict__is not initialized until you use it though, so you shouldn"t worry about the space occupied by an empty dictionary for each instance you create.) If you don"t need this extra space, you can add the phrase "__slots__ = []" to your class.
os.listdir()- list in the current directory
With listdir in os module you get the files and the folders in the current dir
import os
arr = os.listdir()
print(arr)
>>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]
arr = os.listdir("c:\files")
globfrom glob
with glob you can specify a type of file to list like this
import glob
txtfiles = []
for file in glob.glob("*.txt"):
txtfiles.append(file)
glob in a list comprehensionmylist = [f for f in glob.glob("*.txt")]
import os
from os import listdir
from os.path import isfile, join
cwd = os.getcwd()
onlyfiles = [os.path.join(cwd, f) for f in os.listdir(cwd) if
os.path.isfile(os.path.join(cwd, f))]
print(onlyfiles)
["G:\getfilesname\getfilesname.py", "G:\getfilesname\example.txt"]
Getting the full path name with
os.path.abspath
You get the full path in return
import os
files_path = [os.path.abspath(x) for x in os.listdir()]
print(files_path)
["F:\documentiapplications.txt", "F:\documenticollections.txt"]
Walk: going through sub directories
os.walk returns the root, the directories list and the files list, that is why I unpacked them in r, d, f in the for loop; it, then, looks for other files and directories in the subfolders of the root and so on until there are no subfolders.
import os
# Getting the current work directory (cwd)
thisdir = os.getcwd()
# r=root, d=directories, f = files
for r, d, f in os.walk(thisdir):
for file in f:
if file.endswith(".docx"):
print(os.path.join(r, file))
os.listdir(): get files in the current directory (Python 2)
In Python 2, if you want the list of the files in the current directory, you have to give the argument as "." or os.getcwd() in the os.listdir method.
import os
arr = os.listdir(".")
print(arr)
>>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]
To go up in the directory tree
# Method 1
x = os.listdir("..")
# Method 2
x= os.listdir("/")
Get files:
os.listdir()in a particular directory (Python 2 and 3)
import os
arr = os.listdir("F:\python")
print(arr)
>>> ["$RECYCLE.BIN", "work.txt", "3ebooks.txt", "documents"]
Get files of a particular subdirectory with
os.listdir()
import os
x = os.listdir("./content")
os.walk(".")- current directory
import os
arr = next(os.walk("."))[2]
print(arr)
>>> ["5bs_Turismo1.pdf", "5bs_Turismo1.pptx", "esperienza.txt"]
next(os.walk("."))andos.path.join("dir", "file")
import os
arr = []
for d,r,f in next(os.walk("F:\_python")):
for file in f:
arr.append(os.path.join(r,file))
for f in arr:
print(files)
>>> F:\_python\dict_class.py
>>> F:\_python\programmi.txt
next(os.walk("F:\")- get the full path - list comprehension
[os.path.join(r,file) for r,d,f in next(os.walk("F:\_python")) for file in f]
>>> ["F:\_python\dict_class.py", "F:\_python\programmi.txt"]
os.walk- get full path - all files in sub dirs**
x = [os.path.join(r,file) for r,d,f in os.walk("F:\_python") for file in f]
print(x)
>>> ["F:\_python\dict.py", "F:\_python\progr.txt", "F:\_python\readl.py"]
os.listdir()- get only txt files
arr_txt = [x for x in os.listdir() if x.endswith(".txt")]
print(arr_txt)
>>> ["work.txt", "3ebooks.txt"]
Using
globto get the full path of the files
If I should need the absolute path of the files:
from path import path
from glob import glob
x = [path(f).abspath() for f in glob("F:\*.txt")]
for f in x:
print(f)
>>> F:acquistionline.txt
>>> F:acquisti_2018.txt
>>> F:ootstrap_jquery_ecc.txt
Using
os.path.isfileto avoid directories in the list
import os.path
listOfFiles = [f for f in os.listdir() if os.path.isfile(f)]
print(listOfFiles)
>>> ["a simple game.py", "data.txt", "decorator.py"]
Using
pathlibfrom Python 3.4
import pathlib
flist = []
for p in pathlib.Path(".").iterdir():
if p.is_file():
print(p)
flist.append(p)
>>> error.PNG
>>> exemaker.bat
>>> guiprova.mp3
>>> setup.py
>>> speak_gui2.py
>>> thumb.PNG
With list comprehension:
flist = [p for p in pathlib.Path(".").iterdir() if p.is_file()]
Alternatively, use pathlib.Path() instead of pathlib.Path(".")
Use glob method in pathlib.Path()
import pathlib
py = pathlib.Path().glob("*.py")
for file in py:
print(file)
>>> stack_overflow_list.py
>>> stack_overflow_list_tkinter.py
Get all and only files with os.walk
import os
x = [i[2] for i in os.walk(".")]
y=[]
for t in x:
for f in t:
y.append(f)
print(y)
>>> ["append_to_list.py", "data.txt", "data1.txt", "data2.txt", "data_180617", "os_walk.py", "READ2.py", "read_data.py", "somma_defaltdic.py", "substitute_words.py", "sum_data.py", "data.txt", "data1.txt", "data_180617"]
Get only files with next and walk in a directory
import os
x = next(os.walk("F://python"))[2]
print(x)
>>> ["calculator.bat","calculator.py"]
Get only directories with next and walk in a directory
import os
next(os.walk("F://python"))[1] # for the current dir use (".")
>>> ["python3","others"]
Get all the subdir names with
walk
for r,d,f in os.walk("F:\_python"):
for dirs in d:
print(dirs)
>>> .vscode
>>> pyexcel
>>> pyschool.py
>>> subtitles
>>> _metaprogramming
>>> .ipynb_checkpoints
os.scandir()from Python 3.5 and greater
import os
x = [f.name for f in os.scandir() if f.is_file()]
print(x)
>>> ["calculator.bat","calculator.py"]
# Another example with scandir (a little variation from docs.python.org)
# This one is more efficient than os.listdir.
# In this case, it shows the files only in the current directory
# where the script is executed.
import os
with os.scandir() as i:
for entry in i:
if entry.is_file():
print(entry.name)
>>> ebookmaker.py
>>> error.PNG
>>> exemaker.bat
>>> guiprova.mp3
>>> setup.py
>>> speakgui4.py
>>> speak_gui2.py
>>> speak_gui3.py
>>> thumb.PNG
Examples:
Ex. 1: How many files are there in the subdirectories?
In this example, we look for the number of files that are included in all the directory and its subdirectories.
import os
def count(dir, counter=0):
"returns number of files in dir and subdirs"
for pack in os.walk(dir):
for f in pack[2]:
counter += 1
return dir + " : " + str(counter) + "files"
print(count("F:\python"))
>>> "F:\python" : 12057 files"
Ex.2: How to copy all files from a directory to another?
A script to make order in your computer finding all files of a type (default: pptx) and copying them in a new folder.
import os
import shutil
from path import path
destination = "F:\file_copied"
# os.makedirs(destination)
def copyfile(dir, filetype="pptx", counter=0):
"Searches for pptx (or other - pptx is the default) files and copies them"
for pack in os.walk(dir):
for f in pack[2]:
if f.endswith(filetype):
fullpath = pack[0] + "\" + f
print(fullpath)
shutil.copy(fullpath, destination)
counter += 1
if counter > 0:
print("-" * 30)
print(" ==> Found in: `" + dir + "` : " + str(counter) + " files
")
for dir in os.listdir():
"searches for folders that starts with `_`"
if dir[0] == "_":
# copyfile(dir, filetype="pdf")
copyfile(dir, filetype="txt")
>>> _compiti18Compito Contabilità 1conti.txt
>>> _compiti18Compito Contabilità 1modula4.txt
>>> _compiti18Compito Contabilità 1moduloa4.txt
>>> ------------------------
>>> ==> Found in: `_compiti18` : 3 files
Ex. 3: How to get all the files in a txt file
In case you want to create a txt file with all the file names:
import os
mylist = ""
with open("filelist.txt", "w", encoding="utf-8") as file:
for eachfile in os.listdir():
mylist += eachfile + "
"
file.write(mylist)
Example: txt with all the files of an hard drive
"""
We are going to save a txt file with all the files in your directory.
We will use the function walk()
"""
import os
# see all the methods of os
# print(*dir(os), sep=", ")
listafile = []
percorso = []
with open("lista_file.txt", "w", encoding="utf-8") as testo:
for root, dirs, files in os.walk("D:\"):
for file in files:
listafile.append(file)
percorso.append(root + "\" + file)
testo.write(file + "
")
listafile.sort()
print("N. of files", len(listafile))
with open("lista_file_ordinata.txt", "w", encoding="utf-8") as testo_ordinato:
for file in listafile:
testo_ordinato.write(file + "
")
with open("percorso.txt", "w", encoding="utf-8") as file_percorso:
for file in percorso:
file_percorso.write(file + "
")
os.system("lista_file.txt")
os.system("lista_file_ordinata.txt")
os.system("percorso.txt")
All the file of C: in one text file
This is a shorter version of the previous code. Change the folder where to start finding the files if you need to start from another position. This code generate a 50 mb on text file on my computer with something less then 500.000 lines with files with the complete path.
import os
with open("file.txt", "w", encoding="utf-8") as filewrite:
for r, d, f in os.walk("C:\"):
for file in f:
filewrite.write(f"{r + file}
")
How to write a file with all paths in a folder of a type
With this function you can create a txt file that will have the name of a type of file that you look for (ex. pngfile.txt) with all the full path of all the files of that type. It can be useful sometimes, I think.
import os
def searchfiles(extension=".ttf", folder="H:\"):
"Create a txt file with all the file of a type"
with open(extension[1:] + "file.txt", "w", encoding="utf-8") as filewrite:
for r, d, f in os.walk(folder):
for file in f:
if file.endswith(extension):
filewrite.write(f"{r + file}
")
# looking for png file (fonts) in the hard disk H:
searchfiles(".png", "H:\")
>>> H:4bs_18Dolphins5.png
>>> H:4bs_18Dolphins6.png
>>> H:4bs_18Dolphins7.png
>>> H:5_18marketing htmlassetsimageslogo2.png
>>> H:7z001.png
>>> H:7z002.png
(New) Find all files and open them with tkinter GUI
I just wanted to add in this 2019 a little app to search for all files in a dir and be able to open them by doubleclicking on the name of the file in the list.
import tkinter as tk
import os
def searchfiles(extension=".txt", folder="H:\"):
"insert all files in the listbox"
for r, d, f in os.walk(folder):
for file in f:
if file.endswith(extension):
lb.insert(0, r + "\" + file)
def open_file():
os.startfile(lb.get(lb.curselection()[0]))
root = tk.Tk()
root.geometry("400x400")
bt = tk.Button(root, text="Search", command=lambda:searchfiles(".png", "H:\"))
bt.pack()
lb = tk.Listbox(root)
lb.pack(fill="both", expand=1)
lb.bind("<Double-Button>", lambda x: open_file())
root.mainloop()
This is the behaviour to adopt when the referenced object is deleted. It is not specific to Django; this is an SQL standard. Although Django has its own implementation on top of SQL. (1)
There are seven possible actions to take when such event occurs:
CASCADE: When the referenced object is deleted, also delete the objects that have references to it (when you remove a blog post for instance, you might want to delete comments as well). SQL equivalent: CASCADE.PROTECT: Forbid the deletion of the referenced object. To delete it you will have to delete all objects that reference it manually. SQL equivalent: RESTRICT.RESTRICT: (introduced in Django 3.1) Similar behavior as PROTECT that matches SQL"s RESTRICT more accurately. (See django documentation example)SET_NULL: Set the reference to NULL (requires the field to be nullable). For instance, when you delete a User, you might want to keep the comments he posted on blog posts, but say it was posted by an anonymous (or deleted) user. SQL equivalent: SET NULL.SET_DEFAULT: Set the default value. SQL equivalent: SET DEFAULT.SET(...): Set a given value. This one is not part of the SQL standard and is entirely handled by Django.DO_NOTHING: Probably a very bad idea since this would create integrity issues in your database (referencing an object that actually doesn"t exist). SQL equivalent: NO ACTION. (2)Source: Django documentation
See also the documentation of PostgreSQL for instance.
In most cases, CASCADE is the expected behaviour, but for every ForeignKey, you should always ask yourself what is the expected behaviour in this situation. PROTECT and SET_NULL are often useful. Setting CASCADE where it should not, can potentially delete all of your database in cascade, by simply deleting a single user.
Additional note to clarify cascade direction
It"s funny to notice that the direction of the CASCADE action is not clear to many people. Actually, it"s funny to notice that only the CASCADE action is not clear. I understand the cascade behavior might be confusing, however you must think that it is the same direction as any other action. Thus, if you feel that CASCADE direction is not clear to you, it actually means that on_delete behavior is not clear to you.
In your database, a foreign key is basically represented by an integer field which value is the primary key of the foreign object. Let"s say you have an entry comment_A, which has a foreign key to an entry article_B. If you delete the entry comment_A, everything is fine. article_B used to live without comment_A and don"t bother if it"s deleted. However, if you delete article_B, then comment_A panics! It never lived without article_B and needs it, and it"s part of its attributes (article=article_B, but what is article_B???). This is where on_delete steps in, to determine how to resolve this integrity error, either by saying:
PROTECT or RESTRICT in Django/SQL)SET_NULL)CASCADE behavior).SET_DEFAULT, or even SET(...)).DO_NOTHING)I hope it makes cascade direction clearer. :)
Footnotes
(1) Django has its own implementation on top of SQL. And, as mentioned by @JoeMjr2 in the comments below, Django will not create the SQL constraints. If you want the constraints to be ensured by your database (for instance, if your database is used by another application, or if you hang in the database console from time to time), you might want to set the related constraints manually yourself. There is an open ticket to add support for database-level on delete constrains in Django.
(2) Actually, there is one case where
DO_NOTHINGcan be useful: If you want to skip Django"s implementation and implement the constraint yourself at the database-level.
The main distinction between the two methods is:
loc gets rows (and/or columns) with particular labels.
iloc gets rows (and/or columns) at integer locations.
To demonstrate, consider a series s of characters with a non-monotonic integer index:
>>> s = pd.Series(list("abcdef"), index=[49, 48, 47, 0, 1, 2])
49 a
48 b
47 c
0 d
1 e
2 f
>>> s.loc[0] # value at index label 0
"d"
>>> s.iloc[0] # value at index location 0
"a"
>>> s.loc[0:1] # rows at index labels between 0 and 1 (inclusive)
0 d
1 e
>>> s.iloc[0:1] # rows at index location between 0 and 1 (exclusive)
49 a
Here are some of the differences/similarities between s.loc and s.iloc when passed various objects:
| <object> | description | s.loc[<object>] |
s.iloc[<object>] |
|---|---|---|---|
0 |
single item | Value at index label 0 (the string "d") |
Value at index location 0 (the string "a") |
0:1 |
slice | Two rows (labels 0 and 1) |
One row (first row at location 0) |
1:47 |
slice with out-of-bounds end | Zero rows (empty Series) | Five rows (location 1 onwards) |
1:47:-1 |
slice with negative step | three rows (labels 1 back to 47) |
Zero rows (empty Series) |
[2, 0] |
integer list | Two rows with given labels | Two rows with given locations |
s > "e" |
Bool series (indicating which values have the property) | One row (containing "f") |
NotImplementedError |
(s>"e").values |
Bool array | One row (containing "f") |
Same as loc |
999 |
int object not in index | KeyError |
IndexError (out of bounds) |
-1 |
int object not in index | KeyError |
Returns last value in s |
lambda x: x.index[3] |
callable applied to series (here returning 3rd item in index) | s.loc[s.index[3]] |
s.iloc[s.index[3]] |
loc"s label-querying capabilities extend well-beyond integer indexes and it"s worth highlighting a couple of additional examples.
Here"s a Series where the index contains string objects:
>>> s2 = pd.Series(s.index, index=s.values)
>>> s2
a 49
b 48
c 47
d 0
e 1
f 2
Since loc is label-based, it can fetch the first value in the Series using s2.loc["a"]. It can also slice with non-integer objects:
>>> s2.loc["c":"e"] # all rows lying between "c" and "e" (inclusive)
c 47
d 0
e 1
For DateTime indexes, we don"t need to pass the exact date/time to fetch by label. For example:
>>> s3 = pd.Series(list("abcde"), pd.date_range("now", periods=5, freq="M"))
>>> s3
2021-01-31 16:41:31.879768 a
2021-02-28 16:41:31.879768 b
2021-03-31 16:41:31.879768 c
2021-04-30 16:41:31.879768 d
2021-05-31 16:41:31.879768 e
Then to fetch the row(s) for March/April 2021 we only need:
>>> s3.loc["2021-03":"2021-04"]
2021-03-31 17:04:30.742316 c
2021-04-30 17:04:30.742316 d
loc and iloc work the same way with DataFrames as they do with Series. It"s useful to note that both methods can address columns and rows together.
When given a tuple, the first element is used to index the rows and, if it exists, the second element is used to index the columns.
Consider the DataFrame defined below:
>>> import numpy as np
>>> df = pd.DataFrame(np.arange(25).reshape(5, 5),
index=list("abcde"),
columns=["x","y","z", 8, 9])
>>> df
x y z 8 9
a 0 1 2 3 4
b 5 6 7 8 9
c 10 11 12 13 14
d 15 16 17 18 19
e 20 21 22 23 24
Then for example:
>>> df.loc["c": , :"z"] # rows "c" and onwards AND columns up to "z"
x y z
c 10 11 12
d 15 16 17
e 20 21 22
>>> df.iloc[:, 3] # all rows, but only the column at index location 3
a 3
b 8
c 13
d 18
e 23
Sometimes we want to mix label and positional indexing methods for the rows and columns, somehow combining the capabilities of loc and iloc.
For example, consider the following DataFrame. How best to slice the rows up to and including "c" and take the first four columns?
>>> import numpy as np
>>> df = pd.DataFrame(np.arange(25).reshape(5, 5),
index=list("abcde"),
columns=["x","y","z", 8, 9])
>>> df
x y z 8 9
a 0 1 2 3 4
b 5 6 7 8 9
c 10 11 12 13 14
d 15 16 17 18 19
e 20 21 22 23 24
We can achieve this result using iloc and the help of another method:
>>> df.iloc[:df.index.get_loc("c") + 1, :4]
x y z 8
a 0 1 2 3
b 5 6 7 8
c 10 11 12 13
get_loc() is an index method meaning "get the position of the label in this index". Note that since slicing with iloc is exclusive of its endpoint, we must add 1 to this value if we want row "c" as well.
The simplest way to get row counts per group is by calling .size(), which returns a Series:
df.groupby(["col1","col2"]).size()
Usually you want this result as a DataFrame (instead of a Series) so you can do:
df.groupby(["col1", "col2"]).size().reset_index(name="counts")
If you want to find out how to calculate the row counts and other statistics for each group continue reading below.
Consider the following example dataframe:
In [2]: df
Out[2]:
col1 col2 col3 col4 col5 col6
0 A B 0.20 -0.61 -0.49 1.49
1 A B -1.53 -1.01 -0.39 1.82
2 A B -0.44 0.27 0.72 0.11
3 A B 0.28 -1.32 0.38 0.18
4 C D 0.12 0.59 0.81 0.66
5 C D -0.13 -1.65 -1.64 0.50
6 C D -1.42 -0.11 -0.18 -0.44
7 E F -0.00 1.42 -0.26 1.17
8 E F 0.91 -0.47 1.35 -0.34
9 G H 1.48 -0.63 -1.14 0.17
First let"s use .size() to get the row counts:
In [3]: df.groupby(["col1", "col2"]).size()
Out[3]:
col1 col2
A B 4
C D 3
E F 2
G H 1
dtype: int64
Then let"s use .size().reset_index(name="counts") to get the row counts:
In [4]: df.groupby(["col1", "col2"]).size().reset_index(name="counts")
Out[4]:
col1 col2 counts
0 A B 4
1 C D 3
2 E F 2
3 G H 1
When you want to calculate statistics on grouped data, it usually looks like this:
In [5]: (df
...: .groupby(["col1", "col2"])
...: .agg({
...: "col3": ["mean", "count"],
...: "col4": ["median", "min", "count"]
...: }))
Out[5]:
col4 col3
median min count mean count
col1 col2
A B -0.810 -1.32 4 -0.372500 4
C D -0.110 -1.65 3 -0.476667 3
E F 0.475 -0.47 2 0.455000 2
G H -0.630 -0.63 1 1.480000 1
The result above is a little annoying to deal with because of the nested column labels, and also because row counts are on a per column basis.
To gain more control over the output I usually split the statistics into individual aggregations that I then combine using join. It looks like this:
In [6]: gb = df.groupby(["col1", "col2"])
...: counts = gb.size().to_frame(name="counts")
...: (counts
...: .join(gb.agg({"col3": "mean"}).rename(columns={"col3": "col3_mean"}))
...: .join(gb.agg({"col4": "median"}).rename(columns={"col4": "col4_median"}))
...: .join(gb.agg({"col4": "min"}).rename(columns={"col4": "col4_min"}))
...: .reset_index()
...: )
...:
Out[6]:
col1 col2 counts col3_mean col4_median col4_min
0 A B 4 -0.372500 -0.810 -1.32
1 C D 3 -0.476667 -0.110 -1.65
2 E F 2 0.455000 0.475 -0.47
3 G H 1 1.480000 -0.630 -0.63
The code used to generate the test data is shown below:
In [1]: import numpy as np
...: import pandas as pd
...:
...: keys = np.array([
...: ["A", "B"],
...: ["A", "B"],
...: ["A", "B"],
...: ["A", "B"],
...: ["C", "D"],
...: ["C", "D"],
...: ["C", "D"],
...: ["E", "F"],
...: ["E", "F"],
...: ["G", "H"]
...: ])
...:
...: df = pd.DataFrame(
...: np.hstack([keys,np.random.randn(10,4).round(2)]),
...: columns = ["col1", "col2", "col3", "col4", "col5", "col6"]
...: )
...:
...: df[["col3", "col4", "col5", "col6"]] =
...: df[["col3", "col4", "col5", "col6"]].astype(float)
...:
Disclaimer:
If some of the columns that you are aggregating have null values, then you really want to be looking at the group row counts as an independent aggregation for each column. Otherwise you may be misled as to how many records are actually being used to calculate things like the mean because pandas will drop NaN entries in the mean calculation without telling you about it.
Is there a convenient way to calculate percentiles for a sequence or single-dimensional numpy array?
I am looking for something similar to Excel"s percentile function.
I looked in NumPy"s statistics reference, and couldn"t find this. All I could find is the median (50th percentile), but not something more specific.
You have four main options for converting types in pandas:
to_numeric() - provides functionality to safely convert non-numeric types (e.g. strings) to a suitable numeric type. (See also to_datetime() and to_timedelta().)
astype() - convert (almost) any type to (almost) any other type (even if it"s not necessarily sensible to do so). Also allows you to convert to categorial types (very useful).
infer_objects() - a utility method to convert object columns holding Python objects to a pandas type if possible.
convert_dtypes() - convert DataFrame columns to the "best possible" dtype that supports pd.NA (pandas" object to indicate a missing value).
Read on for more detailed explanations and usage of each of these methods.
to_numeric()The best way to convert one or more columns of a DataFrame to numeric values is to use pandas.to_numeric().
This function will try to change non-numeric objects (such as strings) into integers or floating point numbers as appropriate.
The input to to_numeric() is a Series or a single column of a DataFrame.
>>> s = pd.Series(["8", 6, "7.5", 3, "0.9"]) # mixed string and numeric values
>>> s
0 8
1 6
2 7.5
3 3
4 0.9
dtype: object
>>> pd.to_numeric(s) # convert everything to float values
0 8.0
1 6.0
2 7.5
3 3.0
4 0.9
dtype: float64
As you can see, a new Series is returned. Remember to assign this output to a variable or column name to continue using it:
# convert Series
my_series = pd.to_numeric(my_series)
# convert column "a" of a DataFrame
df["a"] = pd.to_numeric(df["a"])
You can also use it to convert multiple columns of a DataFrame via the apply() method:
# convert all columns of DataFrame
df = df.apply(pd.to_numeric) # convert all columns of DataFrame
# convert just columns "a" and "b"
df[["a", "b"]] = df[["a", "b"]].apply(pd.to_numeric)
As long as your values can all be converted, that"s probably all you need.
But what if some values can"t be converted to a numeric type?
to_numeric() also takes an errors keyword argument that allows you to force non-numeric values to be NaN, or simply ignore columns containing these values.
Here"s an example using a Series of strings s which has the object dtype:
>>> s = pd.Series(["1", "2", "4.7", "pandas", "10"])
>>> s
0 1
1 2
2 4.7
3 pandas
4 10
dtype: object
The default behaviour is to raise if it can"t convert a value. In this case, it can"t cope with the string "pandas":
>>> pd.to_numeric(s) # or pd.to_numeric(s, errors="raise")
ValueError: Unable to parse string
Rather than fail, we might want "pandas" to be considered a missing/bad numeric value. We can coerce invalid values to NaN as follows using the errors keyword argument:
>>> pd.to_numeric(s, errors="coerce")
0 1.0
1 2.0
2 4.7
3 NaN
4 10.0
dtype: float64
The third option for errors is just to ignore the operation if an invalid value is encountered:
>>> pd.to_numeric(s, errors="ignore")
# the original Series is returned untouched
This last option is particularly useful when you want to convert your entire DataFrame, but don"t not know which of our columns can be converted reliably to a numeric type. In that case just write:
df.apply(pd.to_numeric, errors="ignore")
The function will be applied to each column of the DataFrame. Columns that can be converted to a numeric type will be converted, while columns that cannot (e.g. they contain non-digit strings or dates) will be left alone.
By default, conversion with to_numeric() will give you either a int64 or float64 dtype (or whatever integer width is native to your platform).
That"s usually what you want, but what if you wanted to save some memory and use a more compact dtype, like float32, or int8?
to_numeric() gives you the option to downcast to either "integer", "signed", "unsigned", "float". Here"s an example for a simple series s of integer type:
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
Downcasting to "integer" uses the smallest possible integer that can hold the values:
>>> pd.to_numeric(s, downcast="integer")
0 1
1 2
2 -7
dtype: int8
Downcasting to "float" similarly picks a smaller than normal floating type:
>>> pd.to_numeric(s, downcast="float")
0 1.0
1 2.0
2 -7.0
dtype: float32
astype()The astype() method enables you to be explicit about the dtype you want your DataFrame or Series to have. It"s very versatile in that you can try and go from one type to the any other.
Just pick a type: you can use a NumPy dtype (e.g. np.int16), some Python types (e.g. bool), or pandas-specific types (like the categorical dtype).
Call the method on the object you want to convert and astype() will try and convert it for you:
# convert all DataFrame columns to the int64 dtype
df = df.astype(int)
# convert column "a" to int64 dtype and "b" to complex type
df = df.astype({"a": int, "b": complex})
# convert Series to float16 type
s = s.astype(np.float16)
# convert Series to Python strings
s = s.astype(str)
# convert Series to categorical type - see docs for more details
s = s.astype("category")
Notice I said "try" - if astype() does not know how to convert a value in the Series or DataFrame, it will raise an error. For example if you have a NaN or inf value you"ll get an error trying to convert it to an integer.
As of pandas 0.20.0, this error can be suppressed by passing errors="ignore". Your original object will be return untouched.
astype() is powerful, but it will sometimes convert values "incorrectly". For example:
>>> s = pd.Series([1, 2, -7])
>>> s
0 1
1 2
2 -7
dtype: int64
These are small integers, so how about converting to an unsigned 8-bit type to save memory?
>>> s.astype(np.uint8)
0 1
1 2
2 249
dtype: uint8
The conversion worked, but the -7 was wrapped round to become 249 (i.e. 28 - 7)!
Trying to downcast using pd.to_numeric(s, downcast="unsigned") instead could help prevent this error.
infer_objects()Version 0.21.0 of pandas introduced the method infer_objects() for converting columns of a DataFrame that have an object datatype to a more specific type (soft conversions).
For example, here"s a DataFrame with two columns of object type. One holds actual integers and the other holds strings representing integers:
>>> df = pd.DataFrame({"a": [7, 1, 5], "b": ["3","2","1"]}, dtype="object")
>>> df.dtypes
a object
b object
dtype: object
Using infer_objects(), you can change the type of column "a" to int64:
>>> df = df.infer_objects()
>>> df.dtypes
a int64
b object
dtype: object
Column "b" has been left alone since its values were strings, not integers. If you wanted to try and force the conversion of both columns to an integer type, you could use df.astype(int) instead.
convert_dtypes()Version 1.0 and above includes a method convert_dtypes() to convert Series and DataFrame columns to the best possible dtype that supports the pd.NA missing value.
Here "best possible" means the type most suited to hold the values. For example, this a pandas integer type if all of the values are integers (or missing values): an object column of Python integer objects is converted to Int64, a column of NumPy int32 values will become the pandas dtype Int32.
With our object DataFrame df, we get the following result:
>>> df.convert_dtypes().dtypes
a Int64
b string
dtype: object
Since column "a" held integer values, it was converted to the Int64 type (which is capable of holding missing values, unlike int64).
Column "b" contained string objects, so was changed to pandas" string dtype.
By default, this method will infer the type from object values in each column. We can change this by passing infer_objects=False:
>>> df.convert_dtypes(infer_objects=False).dtypes
a object
b string
dtype: object
Now column "a" remained an object column: pandas knows it can be described as an "integer" column (internally it ran infer_dtype) but didn"t infer exactly what dtype of integer it should have so did not convert it. Column "b" was again converted to "string" dtype as it was recognised as holding "string" values.
bbox_to_anchor)A legend is positioned inside the bounding box of the axes using the loc argument to plt.legend.
E.g. loc="upper right" places the legend in the upper right corner of the bounding box, which by default extents from (0,0) to (1,1) in axes coordinates (or in bounding box notation (x0,y0, width, height)=(0,0,1,1)).
To place the legend outside of the axes bounding box, one may specify a tuple (x0,y0) of axes coordinates of the lower left corner of the legend.
plt.legend(loc=(1.04,0))
A more versatile approach is to manually specify the bounding box into which the legend should be placed, using the bbox_to_anchor argument. One can restrict oneself to supply only the (x0, y0) part of the bbox. This creates a zero span box, out of which the legend will expand in the direction given by the loc argument. E.g.
plt.legend(bbox_to_anchor=(1.04,1), loc="upper left")
places the legend outside the axes, such that the upper left corner of the legend is at position (1.04,1) in axes coordinates.
Further examples are given below, where additionally the interplay between different arguments like mode and ncols are shown.
l1 = plt.legend(bbox_to_anchor=(1.04,1), borderaxespad=0)
l2 = plt.legend(bbox_to_anchor=(1.04,0), loc="lower left", borderaxespad=0)
l3 = plt.legend(bbox_to_anchor=(1.04,0.5), loc="center left", borderaxespad=0)
l4 = plt.legend(bbox_to_anchor=(0,1.02,1,0.2), loc="lower left",
mode="expand", borderaxespad=0, ncol=3)
l5 = plt.legend(bbox_to_anchor=(1,0), loc="lower right",
bbox_transform=fig.transFigure, ncol=3)
l6 = plt.legend(bbox_to_anchor=(0.4,0.8), loc="upper right")
Details about how to interpret the 4-tuple argument to bbox_to_anchor, as in l4, can be found in this question. The mode="expand" expands the legend horizontally inside the bounding box given by the 4-tuple. For a vertically expanded legend, see this question.
Sometimes it may be useful to specify the bounding box in figure coordinates instead of axes coordinates. This is shown in the example l5 from above, where the bbox_transform argument is used to put the legend in the lower left corner of the figure.
Having placed the legend outside the axes often leads to the undesired situation that it is completely or partially outside the figure canvas.
Solutions to this problem are:
Adjust the subplot parameters
One can adjust the subplot parameters such, that the axes take less space inside the figure (and thereby leave more space to the legend) by using plt.subplots_adjust. E.g.
plt.subplots_adjust(right=0.7)
leaves 30% space on the right-hand side of the figure, where one could place the legend.
Tight layout
Using plt.tight_layout Allows to automatically adjust the subplot parameters such that the elements in the figure sit tight against the figure edges. Unfortunately, the legend is not taken into account in this automatism, but we can supply a rectangle box that the whole subplots area (including labels) will fit into.
plt.tight_layout(rect=[0,0,0.75,1])
Saving the figure with bbox_inches = "tight"
The argument bbox_inches = "tight" to plt.savefig can be used to save the figure such that all artist on the canvas (including the legend) are fit into the saved area. If needed, the figure size is automatically adjusted.
plt.savefig("output.png", bbox_inches="tight")
automatically adjusting the subplot params
A way to automatically adjust the subplot position such that the legend fits inside the canvas without changing the figure size can be found in this answer: Creating figure with exact size and no padding (and legend outside the axes)
Comparison between the cases discussed above:
A figure legend
One may use a legend to the figure instead of the axes, matplotlib.figure.Figure.legend. This has become especially useful for matplotlib version >=2.1, where no special arguments are needed
fig.legend(loc=7)
to create a legend for all artists in the different axes of the figure. The legend is placed using the loc argument, similar to how it is placed inside an axes, but in reference to the whole figure - hence it will be outside the axes somewhat automatically. What remains is to adjust the subplots such that there is no overlap between the legend and the axes. Here the point "Adjust the subplot parameters" from above will be helpful. An example:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0,2*np.pi)
colors=["#7aa0c4","#ca82e1" ,"#8bcd50","#e18882"]
fig, axes = plt.subplots(ncols=2)
for i in range(4):
axes[i//2].plot(x,np.sin(x+i), color=colors[i],label="y=sin(x+{})".format(i))
fig.legend(loc=7)
fig.tight_layout()
fig.subplots_adjust(right=0.75)
plt.show()
Legend inside dedicated subplot axes
An alternative to using bbox_to_anchor would be to place the legend in its dedicated subplot axes (lax).
Since the legend subplot should be smaller than the plot, we may use gridspec_kw={"width_ratios":[4,1]} at axes creation.
We can hide the axes lax.axis("off") but still put a legend in. The legend handles and labels need to obtained from the real plot via h,l = ax.get_legend_handles_labels(), and can then be supplied to the legend in the lax subplot, lax.legend(h,l). A complete example is below.
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = 6,2
fig, (ax,lax) = plt.subplots(ncols=2, gridspec_kw={"width_ratios":[4,1]})
ax.plot(x,y, label="y=sin(x)")
....
h,l = ax.get_legend_handles_labels()
lax.legend(h,l, borderaxespad=0)
lax.axis("off")
plt.tight_layout()
plt.show()
This produces a plot, which is visually pretty similar to the plot from above:
We could also use the first axes to place the legend, but use the bbox_transform of the legend axes,
ax.legend(bbox_to_anchor=(0,0,1,1), bbox_transform=lax.transAxes)
lax.axis("off")
In this approach, we do not need to obtain the legend handles externally, but we need to specify the bbox_to_anchor argument.
loc argument can take numbers instead of strings, which make calls shorter, however, they are not very intuitively mapped to each other. Here is the mapping for reference:
I think you"re almost there, try removing the extra square brackets around the lst"s (Also you don"t need to specify the column names when you"re creating a dataframe from a dict like this):
import pandas as pd
lst1 = range(100)
lst2 = range(100)
lst3 = range(100)
percentile_list = pd.DataFrame(
{"lst1Title": lst1,
"lst2Title": lst2,
"lst3Title": lst3
})
percentile_list
lst1Title lst2Title lst3Title
0 0 0 0
1 1 1 1
2 2 2 2
3 3 3 3
4 4 4 4
5 5 5 5
6 6 6 6
...
If you need a more performant solution you can use np.column_stack rather than zip as in your first attempt, this has around a 2x speedup on the example here, however comes at bit of a cost of readability in my opinion:
import numpy as np
percentile_list = pd.DataFrame(np.column_stack([lst1, lst2, lst3]),
columns=["lst1Title", "lst2Title", "lst3Title"])
To somewhat expand on the earlier answers here, there are a number of details which are commonly overlooked.
subprocess.run() over subprocess.check_call() and friends over subprocess.call() over subprocess.Popen() over os.system() over os.popen()text=True, aka universal_newlines=True.shell=True or shell=False and how it changes quoting and the availability of shell conveniences.sh and BashThese topics are covered in some more detail below.
subprocess.run() or subprocess.check_call()The subprocess.Popen() function is a low-level workhorse but it is tricky to use correctly and you end up copy/pasting multiple lines of code ... which conveniently already exist in the standard library as a set of higher-level wrapper functions for various purposes, which are presented in more detail in the following.
Here"s a paragraph from the documentation:
The recommended approach to invoking subprocesses is to use the
run()function for all use cases it can handle. For more advanced use cases, the underlyingPopeninterface can be used directly.
Unfortunately, the availability of these wrapper functions differs between Python versions.
subprocess.run() was officially introduced in Python 3.5. It is meant to replace all of the following.subprocess.check_output() was introduced in Python 2.7 / 3.1. It is basically equivalent to subprocess.run(..., check=True, stdout=subprocess.PIPE).stdoutsubprocess.check_call() was introduced in Python 2.5. It is basically equivalent to subprocess.run(..., check=True)subprocess.call() was introduced in Python 2.4 in the original subprocess module (PEP-324). It is basically equivalent to subprocess.run(...).returncodesubprocess.Popen()The refactored and extended subprocess.run() is more logical and more versatile than the older legacy functions it replaces. It returns a CompletedProcess object which has various methods which allow you to retrieve the exit status, the standard output, and a few other results and status indicators from the finished subprocess.
subprocess.run() is the way to go if you simply need a program to run and return control to Python. For more involved scenarios (background processes, perhaps with interactive I/O with the Python parent program) you still need to use subprocess.Popen() and take care of all the plumbing yourself. This requires a fairly intricate understanding of all the moving parts and should not be undertaken lightly. The simpler Popen object represents the (possibly still-running) process which needs to be managed from your code for the remainder of the lifetime of the subprocess.
It should perhaps be emphasized that just subprocess.Popen() merely creates a process. If you leave it at that, you have a subprocess running concurrently alongside with Python, so a "background" process. If it doesn"t need to do input or output or otherwise coordinate with you, it can do useful work in parallel with your Python program.
os.system() and os.popen()Since time eternal (well, since Python 2.5) the os module documentation has contained the recommendation to prefer subprocess over os.system():
The
subprocessmodule provides more powerful facilities for spawning new processes and retrieving their results; using that module is preferable to using this function.
The problems with system() are that it"s obviously system-dependent and doesn"t offer ways to interact with the subprocess. It simply runs, with standard output and standard error outside of Python"s reach. The only information Python receives back is the exit status of the command (zero means success, though the meaning of non-zero values is also somewhat system-dependent).
PEP-324 (which was already mentioned above) contains a more detailed rationale for why os.system is problematic and how subprocess attempts to solve those issues.
os.popen() used to be even more strongly discouraged:
Deprecated since version 2.6: This function is obsolete. Use the
subprocessmodule.
However, since sometime in Python 3, it has been reimplemented to simply use subprocess, and redirects to the subprocess.Popen() documentation for details.
check=TrueYou"ll also notice that subprocess.call() has many of the same limitations as os.system(). In regular use, you should generally check whether the process finished successfully, which subprocess.check_call() and subprocess.check_output() do (where the latter also returns the standard output of the finished subprocess). Similarly, you should usually use check=True with subprocess.run() unless you specifically need to allow the subprocess to return an error status.
In practice, with check=True or subprocess.check_*, Python will throw a CalledProcessError exception if the subprocess returns a nonzero exit status.
A common error with subprocess.run() is to omit check=True and be surprised when downstream code fails if the subprocess failed.
On the other hand, a common problem with check_call() and check_output() was that users who blindly used these functions were surprised when the exception was raised e.g. when grep did not find a match. (You should probably replace grep with native Python code anyway, as outlined below.)
All things counted, you need to understand how shell commands return an exit code, and under what conditions they will return a non-zero (error) exit code, and make a conscious decision how exactly it should be handled.
text=True aka universal_newlines=TrueSince Python 3, strings internal to Python are Unicode strings. But there is no guarantee that a subprocess generates Unicode output, or strings at all.
(If the differences are not immediately obvious, Ned Batchelder"s Pragmatic Unicode is recommended, if not outright obligatory, reading. There is a 36-minute video presentation behind the link if you prefer, though reading the page yourself will probably take significantly less time.)
Deep down, Python has to fetch a bytes buffer and interpret it somehow. If it contains a blob of binary data, it shouldn"t be decoded into a Unicode string, because that"s error-prone and bug-inducing behavior - precisely the sort of pesky behavior which riddled many Python 2 scripts, before there was a way to properly distinguish between encoded text and binary data.
With text=True, you tell Python that you, in fact, expect back textual data in the system"s default encoding, and that it should be decoded into a Python (Unicode) string to the best of Python"s ability (usually UTF-8 on any moderately up to date system, except perhaps Windows?)
If that"s not what you request back, Python will just give you bytes strings in the stdout and stderr strings. Maybe at some later point you do know that they were text strings after all, and you know their encoding. Then, you can decode them.
normal = subprocess.run([external, arg],
stdout=subprocess.PIPE, stderr=subprocess.PIPE,
check=True,
text=True)
print(normal.stdout)
convoluted = subprocess.run([external, arg],
stdout=subprocess.PIPE, stderr=subprocess.PIPE,
check=True)
# You have to know (or guess) the encoding
print(convoluted.stdout.decode("utf-8"))
Python 3.7 introduced the shorter and more descriptive and understandable alias text for the keyword argument which was previously somewhat misleadingly called universal_newlines.
shell=True vs shell=FalseWith shell=True you pass a single string to your shell, and the shell takes it from there.
With shell=False you pass a list of arguments to the OS, bypassing the shell.
When you don"t have a shell, you save a process and get rid of a fairly substantial amount of hidden complexity, which may or may not harbor bugs or even security problems.
On the other hand, when you don"t have a shell, you don"t have redirection, wildcard expansion, job control, and a large number of other shell features.
A common mistake is to use shell=True and then still pass Python a list of tokens, or vice versa. This happens to work in some cases, but is really ill-defined and could break in interesting ways.
# XXX AVOID THIS BUG
buggy = subprocess.run("dig +short stackoverflow.com")
# XXX AVOID THIS BUG TOO
broken = subprocess.run(["dig", "+short", "stackoverflow.com"],
shell=True)
# XXX DEFINITELY AVOID THIS
pathological = subprocess.run(["dig +short stackoverflow.com"],
shell=True)
correct = subprocess.run(["dig", "+short", "stackoverflow.com"],
# Probably don"t forget these, too
check=True, text=True)
# XXX Probably better avoid shell=True
# but this is nominally correct
fixed_but_fugly = subprocess.run("dig +short stackoverflow.com",
shell=True,
# Probably don"t forget these, too
check=True, text=True)
The common retort "but it works for me" is not a useful rebuttal unless you understand exactly under what circumstances it could stop working.
Very often, the features of the shell can be replaced with native Python code. Simple Awk or sed scripts should probably simply be translated to Python instead.
To partially illustrate this, here is a typical but slightly silly example which involves many shell features.
cmd = """while read -r x;
do ping -c 3 "$x" | grep "round-trip min/avg/max"
done <hosts.txt"""
# Trivial but horrible
results = subprocess.run(
cmd, shell=True, universal_newlines=True, check=True)
print(results.stdout)
# Reimplement with shell=False
with open("hosts.txt") as hosts:
for host in hosts:
host = host.rstrip("
") # drop newline
ping = subprocess.run(
["ping", "-c", "3", host],
text=True,
stdout=subprocess.PIPE,
check=True)
for line in ping.stdout.split("
"):
if "round-trip min/avg/max" in line:
print("{}: {}".format(host, line))
Some things to note here:
shell=False you don"t need the quoting that the shell requires around strings. Putting quotes anyway is probably an error.The refactored code also illustrates just how much the shell really does for you with a very terse syntax -- for better or for worse. Python says explicit is better than implicit but the Python code is rather verbose and arguably looks more complex than this really is. On the other hand, it offers a number of points where you can grab control in the middle of something else, as trivially exemplified by the enhancement that we can easily include the host name along with the shell command output. (This is by no means challenging to do in the shell, either, but at the expense of yet another diversion and perhaps another process.)
For completeness, here are brief explanations of some of these shell features, and some notes on how they can perhaps be replaced with native Python facilities.
glob.glob() or very often with simple Python string comparisons like for file in os.listdir("."): if not file.endswith(".png"): continue. Bash has various other expansion facilities like .{png,jpg} brace expansion and {1..100} as well as tilde expansion (~ expands to your home directory, and more generally ~account to the home directory of another user)$SHELL or $my_exported_var can sometimes simply be replaced with Python variables. Exported shell variables are available as e.g. os.environ["SHELL"] (the meaning of export is to make the variable available to subprocesses -- a variable which is not available to subprocesses will obviously not be available to Python running as a subprocess of the shell, or vice versa. The env= keyword argument to subprocess methods allows you to define the environment of the subprocess as a dictionary, so that"s one way to make a Python variable visible to a subprocess). With shell=False you will need to understand how to remove any quotes; for example, cd "$HOME" is equivalent to os.chdir(os.environ["HOME"]) without quotes around the directory name. (Very often cd is not useful or necessary anyway, and many beginners omit the double quotes around the variable and get away with it until one day ...)grep "foo" <inputfile >outputfile opens outputfile for writing and inputfile for reading, and passes its contents as standard input to grep, whose standard output then lands in outputfile. This is not generally hard to replace with native Python code.echo foo | nl runs two subprocesses, where the standard output of echo is the standard input of nl (on the OS level, in Unix-like systems, this is a single file handle). If you cannot replace one or both ends of the pipeline with native Python code, perhaps think about using a shell after all, especially if the pipeline has more than two or three processes (though look at the pipes module in the Python standard library or a number of more modern and versatile third-party competitors).ls -l / is equivalent to "ls" "-l" "/" but the quoting around literals is completely optional. Unquoted strings which contain shell metacharacters undergo parameter expansion, whitespace tokenization and wildcard expansion; double quotes prevent whitespace tokenization and wildcard expansion but allow parameter expansions (variable substitution, command substitution, and backslash processing). This is simple in theory but can get bewildering, especially when there are several layers of interpretation (a remote shell command, for example).sh and Bashsubprocess runs your shell commands with /bin/sh unless you specifically request otherwise (except of course on Windows, where it uses the value of the COMSPEC variable). This means that various Bash-only features like arrays, [[ etc are not available.
If you need to use Bash-only syntax, you can
pass in the path to the shell as executable="/bin/bash" (where of course if your Bash is installed somewhere else, you need to adjust the path).
subprocess.run("""
# This for loop syntax is Bash only
for((i=1;i<=$#;i++)); do
# Arrays are Bash-only
array[i]+=123
done""",
shell=True, check=True,
executable="/bin/bash")
subprocess is separate from its parent, and cannot change itA somewhat common mistake is doing something like
subprocess.run("cd /tmp", shell=True)
subprocess.run("pwd", shell=True) # Oops, doesn"t print /tmp
The same thing will happen if the first subprocess tries to set an environment variable, which of course will have disappeared when you run another subprocess, etc.
A child process runs completely separate from Python, and when it finishes, Python has no idea what it did (apart from the vague indicators that it can infer from the exit status and output from the child process). A child generally cannot change the parent"s environment; it cannot set a variable, change the working directory, or, in so many words, communicate with its parent without cooperation from the parent.
The immediate fix in this particular case is to run both commands in a single subprocess;
subprocess.run("cd /tmp; pwd", shell=True)
though obviously this particular use case isn"t very useful; instead, use the cwd keyword argument, or simply os.chdir() before running the subprocess. Similarly, for setting a variable, you can manipulate the environment of the current process (and thus also its children) via
os.environ["foo"] = "bar"
or pass an environment setting to a child process with
subprocess.run("echo "$foo"", shell=True, env={"foo": "bar"})
(not to mention the obvious refactoring subprocess.run(["echo", "bar"]); but echo is a poor example of something to run in a subprocess in the first place, of course).
This is slightly dubious advice; there are certainly situations where it does make sense or is even an absolute requirement to run the Python interpreter as a subprocess from a Python script. But very frequently, the correct approach is simply to import the other Python module into your calling script and call its functions directly.
If the other Python script is under your control, and it isn"t a module, consider turning it into one. (This answer is too long already so I will not delve into details here.)
If you need parallelism, you can run Python functions in subprocesses with the multiprocessing module. There is also threading which runs multiple tasks in a single process (which is more lightweight and gives you more control, but also more constrained in that threads within a process are tightly coupled, and bound to a single GIL.)
TL;DR
def square_list(n):
the_list = [] # Replace
for x in range(n):
y = x * x
the_list.append(y) # these
return the_list # lines
def square_yield(n):
for x in range(n):
y = x * x
yield y # with this one.
Whenever you find yourself building a list from scratch, yield each piece instead.
This was my first "aha" moment with yield.
yield is a sugary way to say
build a series of stuff
Same behavior:
>>> for square in square_list(4):
... print(square)
...
0
1
4
9
>>> for square in square_yield(4):
... print(square)
...
0
1
4
9
Different behavior:
Yield is single-pass: you can only iterate through once. When a function has a yield in it we call it a generator function. And an iterator is what it returns. Those terms are revealing. We lose the convenience of a container, but gain the power of a series that"s computed as needed, and arbitrarily long.
Yield is lazy, it puts off computation. A function with a yield in it doesn"t actually execute at all when you call it. It returns an iterator object that remembers where it left off. Each time you call next() on the iterator (this happens in a for-loop) execution inches forward to the next yield. return raises StopIteration and ends the series (this is the natural end of a for-loop).
Yield is versatile. Data doesn"t have to be stored all together, it can be made available one at a time. It can be infinite.
>>> def squares_all_of_them():
... x = 0
... while True:
... yield x * x
... x += 1
...
>>> squares = squares_all_of_them()
>>> for _ in range(4):
... print(next(squares))
...
0
1
4
9
If you need multiple passes and the series isn"t too long, just call list() on it:
>>> list(square_yield(4))
[0, 1, 4, 9]
Brilliant choice of the word yield because both meanings apply:
yield — produce or provide (as in agriculture)
...provide the next data in the series.
yield — give way or relinquish (as in political power)
...relinquish CPU execution until the iterator advances.
To begin, note that quantiles is just the most general term for things like percentiles, quartiles, and medians. You specified five bins in your example, so you are asking qcut for quintiles.
So, when you ask for quintiles with qcut, the bins will be chosen so that you have the same number of records in each bin. You have 30 records, so should have 6 in each bin (your output should look like this, although the breakpoints will differ due to the random draw):
pd.qcut(factors, 5).value_counts()
[-2.578, -0.829] 6
(-0.829, -0.36] 6
(-0.36, 0.366] 6
(0.366, 0.868] 6
(0.868, 2.617] 6
Conversely, for cut you will see something more uneven:
pd.cut(factors, 5).value_counts()
(-2.583, -1.539] 5
(-1.539, -0.5] 5
(-0.5, 0.539] 9
(0.539, 1.578] 9
(1.578, 2.617] 2
That"s because cut will choose the bins to be evenly spaced according to the values themselves and not the frequency of those values. Hence, because you drew from a random normal, you"ll see higher frequencies in the inner bins and fewer in the outer. This is essentially going to be a tabular form of a histogram (which you would expect to be fairly bell shaped with 30 records).
If you want a histogram, you don"t need to attach any "names" to x-values, as on x-axis you would have data bins:
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
np.random.seed(42)
x = np.random.normal(size=1000)
plt.hist(x, density=True, bins=30) # density=False would make counts
plt.ylabel("Probability")
plt.xlabel("Data");
Note, the number of bins=30 was chosen arbitrarily, and there is Freedman–Diaconis rule to be more scientific in choosing the "right" bin width:
, where
IQRis Interquartile range andnis total number of datapoints to plot
So, according to this rule one may calculate number of bins as:
q25, q75 = np.percentile(x,[.25,.75])
bin_width = 2*(q75 - q25)*len(x)**(-1/3)
bins = round((x.max() - x.min())/bin_width)
print("Freedman–Diaconis number of bins:", bins)
plt.hist(x, bins = bins);
Freedman–Diaconis number of bins: 82
And finally you can make your histogram a bit fancier with PDF line, titles, and legend:
import scipy.stats as st
plt.hist(x, density=True, bins=82, label="Data")
mn, mx = plt.xlim()
plt.xlim(mn, mx)
kde_xs = np.linspace(mn, mx, 300)
kde = st.gaussian_kde(x)
plt.plot(kde_xs, kde.pdf(kde_xs), label="PDF")
plt.legend(loc="upper left")
plt.ylabel("Probability")
plt.xlabel("Data")
plt.title("Histogram");
However, if you have limited number of data points, like in OP, a bar plot would make more sense to represent your data. Then you may attach labels to x-axis:
x = np.arange(3)
plt.bar(x, height=[1,2,3])
plt.xticks(x, ["a","b","c"])
To save some folks some time, here is a list I extracted from a small corpus. I do not know if it is complete, but it should have most (if not all) of the help definitions from upenn_tagset...
CC: conjunction, coordinating
& "n and both but either et for less minus neither nor or plus so
therefore times v. versus vs. whether yet
CD: numeral, cardinal
mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one forty-
seven 1987 twenty "79 zero two 78-degrees eighty-four IX "60s .025
fifteen 271,124 dozen quintillion DM2,000 ...
DT: determiner
all an another any both del each either every half la many much nary
neither no some such that the them these this those
EX: existential there
there
IN: preposition or conjunction, subordinating
astride among upon whether out inside pro despite on by throughout
below within for towards near behind atop around if like until below
next into if beside ...
JJ: adjective or numeral, ordinal
third ill-mannered pre-war regrettable oiled calamitous first separable
ectoplasmic battery-powered participatory fourth still-to-be-named
multilingual multi-disciplinary ...
JJR: adjective, comparative
bleaker braver breezier briefer brighter brisker broader bumper busier
calmer cheaper choosier cleaner clearer closer colder commoner costlier
cozier creamier crunchier cuter ...
JJS: adjective, superlative
calmest cheapest choicest classiest cleanest clearest closest commonest
corniest costliest crassest creepiest crudest cutest darkest deadliest
dearest deepest densest dinkiest ...
LS: list item marker
A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005
SP-44007 Second Third Three Two * a b c d first five four one six three
two
MD: modal auxiliary
can cannot could couldn"t dare may might must need ought shall should
shouldn"t will would
NN: noun, common, singular or mass
common-carrier cabbage knuckle-duster Casino afghan shed thermostat
investment slide humour falloff slick wind hyena override subhumanity
machinist ...
NNP: noun, proper, singular
Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos
Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA
Shannon A.K.C. Meltex Liverpool ...
NNS: noun, common, plural
undergraduates scotches bric-a-brac products bodyguards facets coasts
divestitures storehouses designs clubs fragrances averages
subjectivists apprehensions muses factory-jobs ...
PDT: pre-determiner
all both half many quite such sure this
POS: genitive marker
" "s
PRP: pronoun, personal
hers herself him himself hisself it itself me myself one oneself ours
ourselves ownself self she thee theirs them themselves they thou thy us
PRP$: pronoun, possessive
her his mine my our ours their thy your
RB: adverb
occasionally unabatingly maddeningly adventurously professedly
stirringly prominently technologically magisterially predominately
swiftly fiscally pitilessly ...
RBR: adverb, comparative
further gloomier grander graver greater grimmer harder harsher
healthier heavier higher however larger later leaner lengthier less-
perfectly lesser lonelier longer louder lower more ...
RBS: adverb, superlative
best biggest bluntest earliest farthest first furthest hardest
heartiest highest largest least less most nearest second tightest worst
RP: particle
aboard about across along apart around aside at away back before behind
by crop down ever fast for forth from go high i.e. in into just later
low more off on open out over per pie raising start teeth that through
under unto up up-pp upon whole with you
TO: "to" as preposition or infinitive marker
to
UH: interjection
Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amen
huh howdy uh dammit whammo shucks heck anyways whodunnit honey golly
man baby diddle hush sonuvabitch ...
VB: verb, base form
ask assemble assess assign assume atone attention avoid bake balkanize
bank begin behold believe bend benefit bevel beware bless boil bomb
boost brace break bring broil brush build ...
VBD: verb, past tense
dipped pleaded swiped regummed soaked tidied convened halted registered
cushioned exacted snubbed strode aimed adopted belied figgered
speculated wore appreciated contemplated ...
VBG: verb, present participle or gerund
telegraphing stirring focusing angering judging stalling lactating
hankerin" alleging veering capping approaching traveling besieging
encrypting interrupting erasing wincing ...
VBN: verb, past participle
multihulled dilapidated aerosolized chaired languished panelized used
experimented flourished imitated reunifed factored condensed sheared
unsettled primed dubbed desired ...
VBP: verb, present tense, not 3rd person singular
predominate wrap resort sue twist spill cure lengthen brush terminate
appear tend stray glisten obtain comprise detest tease attract
emphasize mold postpone sever return wag ...
VBZ: verb, present tense, 3rd person singular
bases reconstructs marks mixes displeases seals carps weaves snatches
slumps stretches authorizes smolders pictures emerges stockpiles
seduces fizzes uses bolsters slaps speaks pleads ...
WDT: WH-determiner
that what whatever which whichever
WP: WH-pronoun
that what whatever whatsoever which who whom whosoever
WRB: Wh-adverb
how however whence whenever where whereby whereever wherein whereof why
For each of your dataframe column, you could get quantile with:
q = df["col"].quantile(0.99)
and then filter with:
df[df["col"] < q]
If one need to remove lower and upper outliers, combine condition with an AND statement:
q_low = df["col"].quantile(0.01)
q_hi = df["col"].quantile(0.99)
df_filtered = df[(df["col"] < q_hi) & (df["col"] > q_low)]
Python is a very versatile language. You may print variables by different methods. I have listed below five methods. You may use them according to your convenience.
Example:
a = 1
b = "ball"
Method 1:
print("I have %d %s" % (a, b))
Method 2:
print("I have", a, b)
Method 3:
print("I have {} {}".format(a, b))
Method 4:
print("I have " + str(a) + " " + b)
Method 5:
print(f"I have {a} {b}")
The output would be:
I have 1 ball
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