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Pandas DataFrame all() Method

Pandas all() method is used to check whether all the elements of a DataFrame are zero or not. It returns either series or DataFrame containing True and False values, if the level parameter is specified then it returns DataFrame, Series otherwise.

We can check DataFrame elements to its axis, either based on row or column by specifying the axis parameter in the pandas.DataFrame.all() method.

Let's understand it by using examples. The syntax of this method is here.

 

Syntax

DataFrame.all(axis=0, bool_only=None, skipna=True, level=None, **kwargs)

 

Parameters

Parameter Type Default Value Required Description
axis int 0 No {0 or ‘index’, 1 or ‘columns’, None}
bool_only bool None No It includes only boolean columns. If None, will attempt to use everything, then use only boolean data. Not implemented for Series.
skipna bool True No It is used to exclude NA/null values in the DataFrame. If the entire row/column is NA and skipna is True, then the result will be True, as for an empty row/column. If skipna is False, then NA is treated as True, because these are not equal to zero.
level int or level name None No If the axis is a MultiIndex (hierarchical), count along with a particular level, collapsing into a Series.
**kwargs any None No This keyword has no additional effect but might be accepted for compatibility with Python NumPy.

 

Return Value

It returns DataFrame if the level parameter is specified; otherwise, Series is returned.

 

Example with Jupyter Notebook

Let's take an example to understand the use of pandas.DataFrame.all() method. Following is the code that is executed by using the Jupyter notebookIf you are familiar with Jupyter, See the screenshot below.

 

python-pandas-dataframe-all-method

 

Example: Check if any non-zero is present in the DataFrame

Let's check whether the given DataFrame contains zero or empty elements. The dataframe.all() method, by default check column-wise and returns False if any column contains zero or empty element. See the example here. This example is similar to the above code, executed by using Jupyter notebook. If you are not familiar with Jupyter then see this code to understand.

# import pandas library
import pandas as pd
# Create DataFrame of data
df = pd.DataFrame({
    'A': [12, 13, 14, 15],
    'B': [20, 40, 50, 30],
    'C': [-100, 40, -10, 0]
})
# print dataframe
print(df)
print("\nApplying all() method...\n")
# all() method
df2 = df.all()
print(df2)

Output:     

     A   B    C
0  12  20 -100
1  13  40   40
2  14  50  -10
3  15  30    0

Applying all() method...

A     True
B     True
C    False
dtype: bool

 

 

Example: Check Row-wise, if any non-zero element is present

We can check for the zero or empty elements on the row-wise axis by setting the axis parameter as "columns" as we did in the below example.

# import pandas library
import pandas as pd
# Create DataFrame of data
df = pd.DataFrame({
    'A': [12, 13, 14, 15],
    'B': [20, 40, 50, 30],
    'C': [-100, 40, -10, 0]
})
# print dataframe
print(df)
print("\nApplying all() method...\n")
# all() method
df2 = df.all(axis='columns') # row-wise checking
print(df2)

 

Output:

     A   B    C
0  12  20 -100
1  13  40   40
2  14  50  -10
3  15  30    0

Applying all() method...

0     True
1     True
2     True
3    False
dtype: bool

 

Example: Get DataFrame 

In all the above examples, we get Series as a result but we can get DataFrame by setting the level parameter to the DataFrame.all() method. See the example here.

# import pandas library
import pandas as pd
# Create DataFrame of data
df = pd.DataFrame({
    'A': [12, 13, 14, 15],
    'B': [20, 40, 50, 30],
    'C': [-100, 40, -10, 0]
})
# print dataframe
print(df)
print("\nApplying all() method...\n")
# all() method
df2 = df.all(level=0) # returns a DataFrame
print(df2)

Output:

      A   B    C
0  12  20 -100
1  13  40   40
2  14  50  -10
3  15  30    0

Applying all() method...

       A     B        C
0  True  True   True
1  True  True   True
2  True  True   True
3  True  True  False

 

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