We can use OS module in python provides functions for interacting with the operating system and provides a portable way to use operating system specific functionality. We can go to the current working directory with os.getcwd () and rename the files with the os.rame () method os.rame () .
Below is the Python implementation:
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Exit:
Is there a way to rename a dictionary key, without reassigning its value to a new name and removing the old name key; and without iterating through dict key/value?
In case of OrderedDict, do the same, while keeping that key"s position.
I want to change a.txt to b.kml.
I"m trying to rename some files in a directory using Python.
Say I have a file called CHEESE_CHEESE_TYPE.*** and want to remove CHEESE_ so my resulting filename would be CHEESE_TYPE
I"m trying to use the os.path.split but it"s not working properly. I have also considered using string manipulations, but have not been successful with that either.
I have a conda environment named old_name, how can I change its name to new_name without breaking references?
I"ve got a dataframe called data. How would I rename the only one column header? For example gdp to log(gdp)?
data =
y gdp cap
0 1 2 5
1 2 3 9
2 8 7 2
3 3 4 7
4 6 7 7
5 4 8 3
6 8 2 8
7 9 9 10
8 6 6 4
9 10 10 7
I would like to change a name of specific fields in a model:
class Foo(models.Model):
name = models.CharField()
rel = models.ForeignKey(Bar)
should change to:
class Foo(models.Model):
full_name = models.CharField()
odd_relation = models.ForeignKey(Bar)
What"s the easiest way to do this using South?
I"ve a csv file without header, with a DateTime index. I want to rename the index and column name, but with df.rename() only the column name is renamed. Bug? I"m on version 0.12.0
In [2]: df = pd.read_csv(r"D:DataDataTimeSeries_csv//seriesSM.csv", header=None, parse_dates=[[0]], index_col=[0] )
In [3]: df.head()
Out[3]:
1
0
2002-06-18 0.112000
2002-06-22 0.190333
2002-06-26 0.134000
2002-06-30 0.093000
2002-07-04 0.098667
In [4]: df.rename(index={0:"Date"}, columns={1:"SM"}, inplace=True)
In [5]: df.head()
Out[5]:
SM
0
2002-06-18 0.112000
2002-06-22 0.190333
2002-06-26 0.134000
2002-06-30 0.093000
2002-07-04 0.098667
I"ve been hunting for an answer to this on South"s site, Google, and SO, but couldn"t find a simple way to do this.
I want to rename a Django model using South. Say you have the following:
class Foo(models.Model):
name = models.CharField()
class FooTwo(models.Model):
name = models.CharField()
foo = models.ForeignKey(Foo)
and you want to convert Foo to Bar, namely
class Bar(models.Model):
name = models.CharField()
class FooTwo(models.Model):
name = models.CharField()
foo = models.ForeignKey(Bar)
To keep it simple, I"m just trying to change the name from Foo to Bar, but ignore the foo member in FooTwo for now.
What"s the easiest way to do this using South?
db.rename_table("city_citystate", "geo_citystate"), but I"m not sure how to fix the foreign key in this case.I have an environment called doors and I would like to rename it to django for the virtualenvwrapper.
I"ve noticed that if I just rename the folder ~/.virtualenvs/doors to django, I can now call workon django, but the environment still says (doors)[email protected].
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.
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.
For the simple case of:
The simplest solution is:
df[["A", "B"]] = df["AB"].str.split(" ", 1, expand=True)
You must use expand=True if your strings have a non-uniform number of splits and you want None to replace the missing values.
Notice how, in either case, the .tolist() method is not necessary. Neither is zip().
Andy Hayden"s solution is most excellent in demonstrating the power of the str.extract() method.
But for a simple split over a known separator (like, splitting by dashes, or splitting by whitespace), the .str.split() method is enough1. It operates on a column (Series) of strings, and returns a column (Series) of lists:
>>> import pandas as pd
>>> df = pd.DataFrame({"AB": ["A1-B1", "A2-B2"]})
>>> df
AB
0 A1-B1
1 A2-B2
>>> df["AB_split"] = df["AB"].str.split("-")
>>> df
AB AB_split
0 A1-B1 [A1, B1]
1 A2-B2 [A2, B2]
1: If you"re unsure what the first two parameters of .str.split() do,
I recommend the docs for the plain Python version of the method.
But how do you go from:
to:
Well, we need to take a closer look at the .str attribute of a column.
It"s a magical object that is used to collect methods that treat each element in a column as a string, and then apply the respective method in each element as efficient as possible:
>>> upper_lower_df = pd.DataFrame({"U": ["A", "B", "C"]})
>>> upper_lower_df
U
0 A
1 B
2 C
>>> upper_lower_df["L"] = upper_lower_df["U"].str.lower()
>>> upper_lower_df
U L
0 A a
1 B b
2 C c
But it also has an "indexing" interface for getting each element of a string by its index:
>>> df["AB"].str[0]
0 A
1 A
Name: AB, dtype: object
>>> df["AB"].str[1]
0 1
1 2
Name: AB, dtype: object
Of course, this indexing interface of .str doesn"t really care if each element it"s indexing is actually a string, as long as it can be indexed, so:
>>> df["AB"].str.split("-", 1).str[0]
0 A1
1 A2
Name: AB, dtype: object
>>> df["AB"].str.split("-", 1).str[1]
0 B1
1 B2
Name: AB, dtype: object
Then, it"s a simple matter of taking advantage of the Python tuple unpacking of iterables to do
>>> df["A"], df["B"] = df["AB"].str.split("-", 1).str
>>> df
AB AB_split A B
0 A1-B1 [A1, B1] A1 B1
1 A2-B2 [A2, B2] A2 B2
Of course, getting a DataFrame out of splitting a column of strings is so useful that the .str.split() method can do it for you with the expand=True parameter:
>>> df["AB"].str.split("-", 1, expand=True)
0 1
0 A1 B1
1 A2 B2
So, another way of accomplishing what we wanted is to do:
>>> df = df[["AB"]]
>>> df
AB
0 A1-B1
1 A2-B2
>>> df.join(df["AB"].str.split("-", 1, expand=True).rename(columns={0:"A", 1:"B"}))
AB A B
0 A1-B1 A1 B1
1 A2-B2 A2 B2
The expand=True version, although longer, has a distinct advantage over the tuple unpacking method. Tuple unpacking doesn"t deal well with splits of different lengths:
>>> df = pd.DataFrame({"AB": ["A1-B1", "A2-B2", "A3-B3-C3"]})
>>> df
AB
0 A1-B1
1 A2-B2
2 A3-B3-C3
>>> df["A"], df["B"], df["C"] = df["AB"].str.split("-")
Traceback (most recent call last):
[...]
ValueError: Length of values does not match length of index
>>>
But expand=True handles it nicely by placing None in the columns for which there aren"t enough "splits":
>>> df.join(
... df["AB"].str.split("-", expand=True).rename(
... columns={0:"A", 1:"B", 2:"C"}
... )
... )
AB A B C
0 A1-B1 A1 B1 None
1 A2-B2 A2 B2 None
2 A3-B3-C3 A3 B3 C3
rootis the old (pre-conda 4.4) name for the main environment; after conda 4.4, it was renamed to bebase. source
In most cases what you want to do when you say that you want to update Anaconda is to execute the command:
conda update --all
(But this should be preceeded by conda update -n base conda so you have the latest conda version installed)
This will update all packages in the current environment to the latest version -- with the small print being that it may use an older version of some packages in order to satisfy dependency constraints (often this won"t be necessary and when it is necessary the package plan solver will do its best to minimize the impact).
This needs to be executed from the command line, and the best way to get there is from Anaconda Navigator, then the "Environments" tab, then click on the triangle beside the base environment, selecting "Open Terminal":
This operation will only update the one selected environment (in this case, the base environment). If you have other environments you"d like to update you can repeat the process above, but first click on the environment. When it is selected there is a triangular marker on the right (see image above, step 3). Or from the command line you can provide the environment name (-n envname) or path (-p /path/to/env), for example to update your dspyr environment from the screenshot above:
conda update -n dspyr --all
If you are only interested in updating an individual package then simply click on the blue arrow or blue version number in Navigator, e.g. for astroid or astropy in the screenshot above, and this will tag those packages for an upgrade. When you are done you need to click the "Apply" button:
Or from the command line:
conda update astroid astropy
If you don"t care about package versions and just want "the latest set of all packages in the standard Anaconda Distribution, so long as they work together", then you should take a look at this gist.
In most cases updating the Anaconda package in the package list will have a surprising result: you may actually downgrade many packages (in fact, this is likely if it indicates the version as custom). The gist above provides details.
Your base environment is probably not a good place to try and manage an exact set of packages: it is going to be a dynamic working space with new packages installed and packages randomly updated. If you need an exact set of packages then create a conda environment to hold them. Thanks to the conda package cache and the way file linking is used doing this is typically i) fast and ii) consumes very little additional disk space. E.g.
conda create -n myspecialenv -c bioconda -c conda-forge python=3.5 pandas beautifulsoup seaborn nltk
The conda documentation has more details and examples.
None of this is going to help with updating packages that have been installed from PyPI via pip or any packages installed using python setup.py install. conda list will give you some hints about the pip-based Python packages you have in an environment, but it won"t do anything special to update them.
It is pretty much exactly the same story, with the exception that you may not be able to update the base environment if it was installed by someone else (say to /opt/anaconda/latest). If you"re not able to update the environments you are using you should be able to clone and then update:
conda create -n myenv --clone base
conda update -n myenv --all
First consider if you really need to iterate over rows in a DataFrame. See this answer for alternatives.
If you still need to iterate over rows, you can use methods below. Note some important caveats which are not mentioned in any of the other answers.
for index, row in df.iterrows():
print(row["c1"], row["c2"])
for row in df.itertuples(index=True, name="Pandas"):
print(row.c1, row.c2)
itertuples() is supposed to be faster than iterrows()
But be aware, according to the docs (pandas 0.24.2 at the moment):
iterrows: dtype might not match from row to row
Because iterrows returns a Series for each row, it does not preserve dtypes across the rows (dtypes are preserved across columns for DataFrames). To preserve dtypes while iterating over the rows, it is better to use itertuples() which returns namedtuples of the values and which is generally much faster than iterrows()
iterrows: Do not modify rows
You should never modify something you are iterating over. This is not guaranteed to work in all cases. Depending on the data types, the iterator returns a copy and not a view, and writing to it will have no effect.
Use DataFrame.apply() instead:
new_df = df.apply(lambda x: x * 2)
itertuples:
The column names will be renamed to positional names if they are invalid Python identifiers, repeated, or start with an underscore. With a large number of columns (>255), regular tuples are returned.
See pandas docs on iteration for more details.
There are many ways to do that:
Option 1. Using selectExpr.
data = sqlContext.createDataFrame([("Alberto", 2), ("Dakota", 2)],
["Name", "askdaosdka"])
data.show()
data.printSchema()
# Output
#+-------+----------+
#| Name|askdaosdka|
#+-------+----------+
#|Alberto| 2|
#| Dakota| 2|
#+-------+----------+
#root
# |-- Name: string (nullable = true)
# |-- askdaosdka: long (nullable = true)
df = data.selectExpr("Name as name", "askdaosdka as age")
df.show()
df.printSchema()
# Output
#+-------+---+
#| name|age|
#+-------+---+
#|Alberto| 2|
#| Dakota| 2|
#+-------+---+
#root
# |-- name: string (nullable = true)
# |-- age: long (nullable = true)
Option 2. Using withColumnRenamed, notice that this method allows you to "overwrite" the same column. For Python3, replace xrange with range.
from functools import reduce
oldColumns = data.schema.names
newColumns = ["name", "age"]
df = reduce(lambda data, idx: data.withColumnRenamed(oldColumns[idx], newColumns[idx]), xrange(len(oldColumns)), data)
df.printSchema()
df.show()
Option 3. using alias, in Scala you can also use as.
from pyspark.sql.functions import col
data = data.select(col("Name").alias("name"), col("askdaosdka").alias("age"))
data.show()
# Output
#+-------+---+
#| name|age|
#+-------+---+
#|Alberto| 2|
#| Dakota| 2|
#+-------+---+
Option 4. Using sqlContext.sql, which lets you use SQL queries on DataFrames registered as tables.
sqlContext.registerDataFrameAsTable(data, "myTable")
df2 = sqlContext.sql("SELECT Name AS name, askdaosdka as age from myTable")
df2.show()
# Output
#+-------+---+
#| name|age|
#+-------+---+
#|Alberto| 2|
#| Dakota| 2|
#+-------+---+
Explain all in Python?
I keep seeing the variable
__all__set in different__init__.pyfiles.What does this do?
__all__ do?It declares the semantically "public" names from a module. If there is a name in __all__, users are expected to use it, and they can have the expectation that it will not change.
It also will have programmatic effects:
import *__all__ in a module, e.g. module.py:
__all__ = ["foo", "Bar"]
means that when you import * from the module, only those names in the __all__ are imported:
from module import * # imports foo and Bar
Documentation and code autocompletion tools may (in fact, should) also inspect the __all__ to determine what names to show as available from a module.
__init__.py makes a directory a Python packageFrom the docs:
The
__init__.pyfiles are required to make Python treat the directories as containing packages; this is done to prevent directories with a common name, such as string, from unintentionally hiding valid modules that occur later on the module search path.
In the simplest case,
__init__.pycan just be an empty file, but it can also execute initialization code for the package or set the__all__variable.
So the __init__.py can declare the __all__ for a package.
A package is typically made up of modules that may import one another, but that are necessarily tied together with an __init__.py file. That file is what makes the directory an actual Python package. For example, say you have the following files in a package:
package
├── __init__.py
├── module_1.py
└── module_2.py
Let"s create these files with Python so you can follow along - you could paste the following into a Python 3 shell:
from pathlib import Path
package = Path("package")
package.mkdir()
(package / "__init__.py").write_text("""
from .module_1 import *
from .module_2 import *
""")
package_module_1 = package / "module_1.py"
package_module_1.write_text("""
__all__ = ["foo"]
imp_detail1 = imp_detail2 = imp_detail3 = None
def foo(): pass
""")
package_module_2 = package / "module_2.py"
package_module_2.write_text("""
__all__ = ["Bar"]
imp_detail1 = imp_detail2 = imp_detail3 = None
class Bar: pass
""")
And now you have presented a complete api that someone else can use when they import your package, like so:
import package
package.foo()
package.Bar()
And the package won"t have all the other implementation details you used when creating your modules cluttering up the package namespace.
__all__ in __init__.pyAfter more work, maybe you"ve decided that the modules are too big (like many thousands of lines?) and need to be split up. So you do the following:
package
├── __init__.py
├── module_1
│   ├── foo_implementation.py
│   └── __init__.py
└── module_2
├── Bar_implementation.py
└── __init__.py
First make the subpackage directories with the same names as the modules:
subpackage_1 = package / "module_1"
subpackage_1.mkdir()
subpackage_2 = package / "module_2"
subpackage_2.mkdir()
Move the implementations:
package_module_1.rename(subpackage_1 / "foo_implementation.py")
package_module_2.rename(subpackage_2 / "Bar_implementation.py")
create __init__.pys for the subpackages that declare the __all__ for each:
(subpackage_1 / "__init__.py").write_text("""
from .foo_implementation import *
__all__ = ["foo"]
""")
(subpackage_2 / "__init__.py").write_text("""
from .Bar_implementation import *
__all__ = ["Bar"]
""")
And now you still have the api provisioned at the package level:
>>> import package
>>> package.foo()
>>> package.Bar()
<package.module_2.Bar_implementation.Bar object at 0x7f0c2349d210>
And you can easily add things to your API that you can manage at the subpackage level instead of the subpackage"s module level. If you want to add a new name to the API, you simply update the __init__.py, e.g. in module_2:
from .Bar_implementation import *
from .Baz_implementation import *
__all__ = ["Bar", "Baz"]
And if you"re not ready to publish Baz in the top level API, in your top level __init__.py you could have:
from .module_1 import * # also constrained by __all__"s
from .module_2 import * # in the __init__.py"s
__all__ = ["foo", "Bar"] # further constraining the names advertised
and if your users are aware of the availability of Baz, they can use it:
import package
package.Baz()
but if they don"t know about it, other tools (like pydoc) won"t inform them.
You can later change that when Baz is ready for prime time:
from .module_1 import *
from .module_2 import *
__all__ = ["foo", "Bar", "Baz"]
_ versus __all__:By default, Python will export all names that do not start with an _. You certainly could rely on this mechanism. Some packages in the Python standard library, in fact, do rely on this, but to do so, they alias their imports, for example, in ctypes/__init__.py:
import os as _os, sys as _sys
Using the _ convention can be more elegant because it removes the redundancy of naming the names again. But it adds the redundancy for imports (if you have a lot of them) and it is easy to forget to do this consistently - and the last thing you want is to have to indefinitely support something you intended to only be an implementation detail, just because you forgot to prefix an _ when naming a function.
I personally write an __all__ early in my development lifecycle for modules so that others who might use my code know what they should use and not use.
Most packages in the standard library also use __all__.
__all__ makes senseIt makes sense to stick to the _ prefix convention in lieu of __all__ when:
export decoratorThe downside of using __all__ is that you have to write the names of functions and classes being exported twice - and the information is kept separate from the definitions. We could use a decorator to solve this problem.
I got the idea for such an export decorator from David Beazley"s talk on packaging. This implementation seems to work well in CPython"s traditional importer. If you have a special import hook or system, I do not guarantee it, but if you adopt it, it is fairly trivial to back out - you"ll just need to manually add the names back into the __all__
So in, for example, a utility library, you would define the decorator:
import sys
def export(fn):
mod = sys.modules[fn.__module__]
if hasattr(mod, "__all__"):
mod.__all__.append(fn.__name__)
else:
mod.__all__ = [fn.__name__]
return fn
and then, where you would define an __all__, you do this:
$ cat > main.py
from lib import export
__all__ = [] # optional - we create a list if __all__ is not there.
@export
def foo(): pass
@export
def bar():
"bar"
def main():
print("main")
if __name__ == "__main__":
main()
And this works fine whether run as main or imported by another function.
$ cat > run.py
import main
main.main()
$ python run.py
main
And API provisioning with import * will work too:
$ cat > run.py
from main import *
foo()
bar()
main() # expected to error here, not exported
$ python run.py
Traceback (most recent call last):
File "run.py", line 4, in <module>
main() # expected to error here, not exported
NameError: name "main" is not defined
I know object columns type makes the data hard to convert with a pandas function. When I received the data like this, the first thing that came to mind was to "flatten" or unnest the columns .
I am using pandas and python functions for this type of question. If you are worried about the speed of the above solutions, check user3483203"s answer, since it"s using numpy and most of the time numpy is faster . I recommend Cpython and numba if speed matters.
Method 0 [pandas >= 0.25]
Starting from pandas 0.25, if you only need to explode one column, you can use the pandas.DataFrame.explode function:
df.explode("B")
A B
0 1 1
1 1 2
0 2 1
1 2 2
Given a dataframe with an empty list or a NaN in the column. An empty list will not cause an issue, but a NaN will need to be filled with a list
df = pd.DataFrame({"A": [1, 2, 3, 4],"B": [[1, 2], [1, 2], [], np.nan]})
df.B = df.B.fillna({i: [] for i in df.index}) # replace NaN with []
df.explode("B")
A B
0 1 1
0 1 2
1 2 1
1 2 2
2 3 NaN
3 4 NaN
Method 1
apply + pd.Series (easy to understand but in terms of performance not recommended . )
df.set_index("A").B.apply(pd.Series).stack().reset_index(level=0).rename(columns={0:"B"})
Out[463]:
A B
0 1 1
1 1 2
0 2 1
1 2 2
Method 2
Using repeat with DataFrame constructor , re-create your dataframe (good at performance, not good at multiple columns )
df=pd.DataFrame({"A":df.A.repeat(df.B.str.len()),"B":np.concatenate(df.B.values)})
df
Out[465]:
A B
0 1 1
0 1 2
1 2 1
1 2 2
Method 2.1
for example besides A we have A.1 .....A.n. If we still use the method(Method 2) above it is hard for us to re-create the columns one by one .
Solution : join or merge with the index after "unnest" the single columns
s=pd.DataFrame({"B":np.concatenate(df.B.values)},index=df.index.repeat(df.B.str.len()))
s.join(df.drop("B",1),how="left")
Out[477]:
B A
0 1 1
0 2 1
1 1 2
1 2 2
If you need the column order exactly the same as before, add reindex at the end.
s.join(df.drop("B",1),how="left").reindex(columns=df.columns)
Method 3
recreate the list
pd.DataFrame([[x] + [z] for x, y in df.values for z in y],columns=df.columns)
Out[488]:
A B
0 1 1
1 1 2
2 2 1
3 2 2
If more than two columns, use
s=pd.DataFrame([[x] + [z] for x, y in zip(df.index,df.B) for z in y])
s.merge(df,left_on=0,right_index=True)
Out[491]:
0 1 A B
0 0 1 1 [1, 2]
1 0 2 1 [1, 2]
2 1 1 2 [1, 2]
3 1 2 2 [1, 2]
Method 4
using reindex or loc
df.reindex(df.index.repeat(df.B.str.len())).assign(B=np.concatenate(df.B.values))
Out[554]:
A B
0 1 1
0 1 2
1 2 1
1 2 2
#df.loc[df.index.repeat(df.B.str.len())].assign(B=np.concatenate(df.B.values))
Method 5
when the list only contains unique values:
df=pd.DataFrame({"A":[1,2],"B":[[1,2],[3,4]]})
from collections import ChainMap
d = dict(ChainMap(*map(dict.fromkeys, df["B"], df["A"])))
pd.DataFrame(list(d.items()),columns=df.columns[::-1])
Out[574]:
B A
0 1 1
1 2 1
2 3 2
3 4 2
Method 6
using numpy for high performance:
newvalues=np.dstack((np.repeat(df.A.values,list(map(len,df.B.values))),np.concatenate(df.B.values)))
pd.DataFrame(data=newvalues[0],columns=df.columns)
A B
0 1 1
1 1 2
2 2 1
3 2 2
Method 7
using base function itertools cycle and chain: Pure python solution just for fun
from itertools import cycle,chain
l=df.values.tolist()
l1=[list(zip([x[0]], cycle(x[1])) if len([x[0]]) > len(x[1]) else list(zip(cycle([x[0]]), x[1]))) for x in l]
pd.DataFrame(list(chain.from_iterable(l1)),columns=df.columns)
A B
0 1 1
1 1 2
2 2 1
3 2 2
Generalizing to multiple columns
df=pd.DataFrame({"A":[1,2],"B":[[1,2],[3,4]],"C":[[1,2],[3,4]]})
df
Out[592]:
A B C
0 1 [1, 2] [1, 2]
1 2 [3, 4] [3, 4]
Self-def function:
def unnesting(df, explode):
idx = df.index.repeat(df[explode[0]].str.len())
df1 = pd.concat([
pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
df1.index = idx
return df1.join(df.drop(explode, 1), how="left")
unnesting(df,["B","C"])
Out[609]:
B C A
0 1 1 1
0 2 2 1
1 3 3 2
1 4 4 2
All above method is talking about the vertical unnesting and explode , If you do need expend the list horizontal, Check with pd.DataFrame constructor
df.join(pd.DataFrame(df.B.tolist(),index=df.index).add_prefix("B_"))
Out[33]:
A B C B_0 B_1
0 1 [1, 2] [1, 2] 1 2
1 2 [3, 4] [3, 4] 3 4
Updated function
def unnesting(df, explode, axis):
if axis==1:
idx = df.index.repeat(df[explode[0]].str.len())
df1 = pd.concat([
pd.DataFrame({x: np.concatenate(df[x].values)}) for x in explode], axis=1)
df1.index = idx
return df1.join(df.drop(explode, 1), how="left")
else :
df1 = pd.concat([
pd.DataFrame(df[x].tolist(), index=df.index).add_prefix(x) for x in explode], axis=1)
return df1.join(df.drop(explode, 1), how="left")
Test Output
unnesting(df, ["B","C"], axis=0)
Out[36]:
B0 B1 C0 C1 A
0 1 2 1 2 1
1 3 4 3 4 2
Update 2021-02-17 with original explode function
def unnesting(df, explode, axis):
if axis==1:
df1 = pd.concat([df[x].explode() for x in explode], axis=1)
return df1.join(df.drop(explode, 1), how="left")
else :
df1 = pd.concat([
pd.DataFrame(df[x].tolist(), index=df.index).add_prefix(x) for x in explode], axis=1)
return df1.join(df.drop(explode, 1), how="left")
You cannot add an arbitrary column to a DataFrame in Spark. New columns can be created only by using literals (other literal types are described in How to add a constant column in a Spark DataFrame?)
from pyspark.sql.functions import lit
df = sqlContext.createDataFrame(
[(1, "a", 23.0), (3, "B", -23.0)], ("x1", "x2", "x3"))
df_with_x4 = df.withColumn("x4", lit(0))
df_with_x4.show()
## +---+---+-----+---+
## | x1| x2| x3| x4|
## +---+---+-----+---+
## | 1| a| 23.0| 0|
## | 3| B|-23.0| 0|
## +---+---+-----+---+
transforming an existing column:
from pyspark.sql.functions import exp
df_with_x5 = df_with_x4.withColumn("x5", exp("x3"))
df_with_x5.show()
## +---+---+-----+---+--------------------+
## | x1| x2| x3| x4| x5|
## +---+---+-----+---+--------------------+
## | 1| a| 23.0| 0| 9.744803446248903E9|
## | 3| B|-23.0| 0|1.026187963170189...|
## +---+---+-----+---+--------------------+
included using join:
from pyspark.sql.functions import exp
lookup = sqlContext.createDataFrame([(1, "foo"), (2, "bar")], ("k", "v"))
df_with_x6 = (df_with_x5
.join(lookup, col("x1") == col("k"), "leftouter")
.drop("k")
.withColumnRenamed("v", "x6"))
## +---+---+-----+---+--------------------+----+
## | x1| x2| x3| x4| x5| x6|
## +---+---+-----+---+--------------------+----+
## | 1| a| 23.0| 0| 9.744803446248903E9| foo|
## | 3| B|-23.0| 0|1.026187963170189...|null|
## +---+---+-----+---+--------------------+----+
or generated with function / udf:
from pyspark.sql.functions import rand
df_with_x7 = df_with_x6.withColumn("x7", rand())
df_with_x7.show()
## +---+---+-----+---+--------------------+----+-------------------+
## | x1| x2| x3| x4| x5| x6| x7|
## +---+---+-----+---+--------------------+----+-------------------+
## | 1| a| 23.0| 0| 9.744803446248903E9| foo|0.41930610446846617|
## | 3| B|-23.0| 0|1.026187963170189...|null|0.37801881545497873|
## +---+---+-----+---+--------------------+----+-------------------+
Performance-wise, built-in functions (pyspark.sql.functions), which map to Catalyst expression, are usually preferred over Python user defined functions.
If you want to add content of an arbitrary RDD as a column you can
zipWithIndex on RDD and convert it to data framedf = df.withColumnRenamed("colName", "newColName")
.withColumnRenamed("colName2", "newColName2")
Advantage of using this way: With long list of columns you would like to change only few column names. This can be very convenient in these scenarios. Very useful when joining tables with duplicate column names.
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.