.apply()

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Published May 12, 2023
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The apply() method is used to apply a function along one axis of DataFrame data.

Syntax

x = dataframevalue.apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs)
  • dataframevalue is the DataFrame with the source data.
  • This function func does NOT make changes to the original DataFrame object. The result is saved to a variable. In this case, the variable is x.

dataframevalue will be divided into Series objects and each Series is passed to the function x. When axis = 0 (default value), dataframevalue was cut into Series objects vertically. Each Series object has the same index as the DataFrame’s index. When axis = 1, dataframevalue was cut horizontally. Each Series object has the same column name as the DataFrame’s column name.

When result_type = 0 (default value), the final return type depends on the return type of function func. Otherwise, it’s decided by the result_type argument.

More details about DataFrame.apply()‘s parameters:

Parameter Define Usage
func name of the function Function applies to each column or row of the DataFrame data. It can be a customized function. Just call the function by name without brackets “()”
axis 0/1 or index/columns, Default 0 Axis , the function is applied along: 0('index'): apply function to every column; 1('columns'): apply function to every row.
raw bool. Default False Determines the type of the object passed into function 'func': Series or ndarray. False, it passes each row or column as a Series object; True, it passes ndarray objects instead. This will achieve much better performance when applying a Numpy reduction function.
result_type {‘broadcast’, ‘expand’, ‘reduce’, None}. Default None These options only work when axis = 1:'expand', list-like results will be converted into columns; 'reduce', the opposite of 'expand', returns a Series object if possible rather than list-like results; 'broadcast', results will have the original shape of the DataFrame data, also with the same index and columns; None, The default behavior. The return type depends on the return type of the function.
args tuple Additional positional arguments to func.
**kwargs Additional keyword arguments to func.

Example

In the following examples, the .apply() method is used with different parameters:

x and y apply the calc_sum function to df to calculate the sum of each column. z applies the calc_sum function to df to calculate the sum of each row. l applies the np.sqrt function to df to calculate the square root of each value. m applies a lambda function to create a new DataFrame with three column values.

import pandas as pd
import numpy as np
d = {'col 1' : [1,2,3,4], 'col 2' : [5,6,7,8], 'col 3' : [9,10,11,12], 'col 4' : [13,14,15,16]}
df = pd.DataFrame(data = d)
def calc_sum(x):
return x.sum()
x = df.apply(calc_sum)
y = df.apply(calc_sum,axis = 0)
z = df.apply(calc_sum,axis = 1)
l = df.apply(np.sqrt)
m = df.apply(lambda x: pd.Series([0, 1, 2]), axis=1, result_type="expand")
print("Original dataframe:")
print(df)
print("\nx:")
print(x)
print(type(x))
print("\ny:")
print(y)
print(type(y))
print("\nz:")
print(z)
print(type(z))
print("\nl:")
print(l)
print(type(l))
print("\nm:")
print(m)
print(type(m))

The results are the following:

Original dataframe:
col 1 col 2 col 3 col 4
0 1 5 9 13
1 2 6 10 14
2 3 7 11 15
3 4 8 12 16
x:
col 1 10
col 2 26
col 3 42
col 4 58
dtype: int64
<class 'pandas.core.series.Series'>
y:
col 1 10
col 2 26
col 3 42
col 4 58
dtype: int64
<class 'pandas.core.series.Series'>
z:
0 28
1 32
2 36
3 40
dtype: int64
<class 'pandas.core.series.Series'>
l:
col 1 col 2 col 3 col 4
0 1.000000 2.236068 3.000000 3.605551
1 1.414214 2.449490 3.162278 3.741657
2 1.732051 2.645751 3.316625 3.872983
3 2.000000 2.828427 3.464102 4.000000
<class 'pandas.core.frame.DataFrame'>
m:
0 1 2
0 0 1 2
1 0 1 2
2 0 1 2
3 0 1 2
<class 'pandas.core.frame.DataFrame'>

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