Python | Pandas Series.all()
Last Updated :
27 Feb, 2019
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.
import pandas as pd
sr = pd.Series([ 34 , 5 , 13 , 32 , 4 , 15 ])
index_ = [ 'Coca Cola' , 'Sprite' , 'Coke' , 'Fanta' , 'Dew' , 'ThumbsUp' ]
sr.index = index_
print (sr)
|
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.
result = sr. all ()
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.
import pandas as pd
sr = pd.Series([ 51 , 10 , 24 , 18 , 1 , 84 , 12 , 10 , 5 , 24 , 0 ])
index_ = pd.date_range( '2010-10-09 08:45' , periods = 11 , freq = 'Y' )
sr.index = index_
print (sr)
|
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.
result = sr. all ()
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.
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