Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.all()
function return whether all elements are True, potentially over an axis. It returns True
unless there at least one element within a series or along a Dataframe axis that is False
or equivalent (e.g. 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()
function to check if all the values in the given series object is True or non-zero.
# importing pandas as pd import pandas as pd
# Creating the Series sr = pd.Series([ 34 , 5 , 13 , 32 , 4 , 15 ])
# Create the Index index_ = [ 'Coca Cola' , 'Sprite' , 'Coke' , 'Fanta' , 'Dew' , 'ThumbsUp' ]
# set the index sr.index = index_
# Print the series print (sr)
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Output :
Coca Cola 34 Sprite 5 Coke 13 Fanta 32 Dew 4 ThumbsUp 15 dtype: int64
Now we will use Series.all()
function to check if all the values in the given series object is True and non-zero.
# check if all value is True # or non-zero result = sr. all ()
# Print the result print (result)
|
Output :
True
As we can see in the output, the Series.all()
function has successfully returned the True
indicating that all the values in the given series is True or non-zero.
Example #2 : Use Series.all()
function to check if all the values in the given series object is True or non-zero.
# importing pandas as pd import pandas as pd
# Creating the Series sr = pd.Series([ 51 , 10 , 24 , 18 , 1 , 84 , 12 , 10 , 5 , 24 , 0 ])
# Create the Index # apply yearly frequency index_ = pd.date_range( '2010-10-09 08:45' , periods = 11 , freq = 'Y' )
# set the index sr.index = index_
# Print the series print (sr)
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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
Now we will use Series.all()
function to check if all the values in the given series object is True and non-zero.
# check if all value is True # or non-zero result = sr. all ()
# Print the result print (result)
|
Output :
False
As we can see in the output, the Series.all()
function has successfully returned the False
indicating that all the values in the given series is not True or non-zero. One of the values is zero in this series object.