Python | Pandas Series.all ()



Series.all() Pandas Series.all() returns True for all items, possibly , along the axis. It returns True if there is at least one element within the row or along the Dataframe axis that is False or equivalent (such as zero or empty).

Syntax: Series.all (axis = 0, bool_only = None, skipna = True, level = None, ** kwargs)

Parameter:
axis: Indicate which axis or axes should be reduced.
bool_only: Include only boolean columns.
skipna: Exclude NA / null values.
level: If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar.
** kwargs: Additional keywords have no effect but might be accepted for compatibility with NumPy.

Returns: scalar or Series

Example # 1: Use Series.all () to check if all values ​​in a given series object are East different or non-zero.

# import pandas as pd

import pandas as pd

 
# Create series

sr = pd.Series ( [ 34 , 5 , 13 , 32 , 4 , 15 ])

 
# Create index

index_ = [ `Coca Cola` , ` Sprite` , `Coke` , ` Fanta` , `Dew` , `ThumbsUp` ]

  
# set index

sr.index = index_

 
# Print series

print (sr)

Output:

 Coca Cola 34 Sprite 5 Coke 13 Fanta 32 Dew 4 ThumbsUp 15 dtype: int64 

We will now use Series.all () to check if all values ​​in a given series object are True and Non-Null.

# check if all values ​​are True
# or non-zero

result = sr. all ()

 
# Print result

print (result)

Output:

 True 

As we can see in the output, Series.all () successfully returned True indicating that all values ​​in a given row — True or non-zero.

Example # 2: Use Series.all () to check if all values ​​in a given series object are True or non-zero .

Output:

 2010-12-31 08:45:00 51 2011-12-31 08: 45:00 10 2012-12-31 08:45:00 24 2013-12-31 08:45:00 18 2014-12-31 08:45:00 1 2015-12-31 08:45:00 84 2016- 12-31 08:45:00 12 2017-12-31 08:45:00 10 2018-12-31 08:45:00 5 2019-12-31 08:45:00 24 2020-12-31 08:45 : 00 0 Freq: A-DEC, dtype: int64 

We will now use Series.all () to check if all the values ​​in a given series object are True and Nonzero.

# import pandas as pd

import pandas as pd

 
# Create a series

sr = pd.Series ([ 51 , 10 , 24 , 18 , 1 , 84 , 12 < / code> , 10 , 5 , 24 , 0 ])

  
# Create index
# apply annual rate

index_ = pd.date_range ( `2010-10-09 08:45` periods = 11 , freq = `Y` )

 
# set index

sr.index = index_

  
# Print series

print (sr)

# check if all values ​​are True
# or non-zero

result = sr. all ()

 
# Print result

print (result)

Output:

 False 

As we can see in the output, Series.all () successfully returned False indicating that all values ​​in the given row are not True or nonzero. In this series object, one of the values ​​is zero.