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This tutorial explains the difference between the apply(), map(), and methods in Pandas.applymap()

The function associated with applymap()is applied to all elements of a given DataFrame, hence applymap()the method is defined only for DataFrames. Similarly, apply()the function associated with the method can be applied to Seriesall elements of a DataFrame or , hence apply()the method is defined for Series and DataFrame objects. The method in Pandas can be defined map()only for SeriesDataFrame objects.

import pandas as pd

df = pd.DataFrame(
    {
        "Col 1": [30, 40, 50, 60],
        "Col 2": [23, 35, 65, 45],
        "Col 3": [85, 87, 90, 89],
    },
    index=["A", "B", "C", "D"],
)

print(df, "\n")

Output:

   Col 1  Col 2  Col 3
A     30     23     85
B     40     35     87
C     50     65     90
D     60     45     89

We will use the DataFrame shown in the above example dfto explain the difference between the apply(), map(), and methods in Pandas.applymap()

pandas.DataFrame.applymap()

grammar

DataFrame.applymap(func, na_action=None)

It funcapplies the function DataFrameto each element of .

Example: Using applymap()the method to change the elements in a DataFrame

import pandas as pd

df = pd.DataFrame(
    {
        "Col 1": [30, 40, 50, 60],
        "Col 2": [23, 35, 65, 45],
        "Col 3": [85, 87, 90, 89],
    },
    index=["A", "B", "C", "D"],
)

print("Initial DataFrame:")
print(df, "\n")

scaled_df = df.applymap(lambda a: a * 10)

print("Scaled DataFrame:")
print(scaled_df, "\n")

Output:

Initial DataFrame:
   Col 1  Col 2  Col 3
A     30     23     85
B     40     35     87
C     50     65     90
D     60     45     89

Scaled DataFrame:
   Col 1  Col 2  Col 3
A    300    230    850
B    400    350    870
C    500    650    900
D    600    450    890

It dfmultiplies each element in the DataFrame and stores the result scaled_dfin the DataFrame. We lambdapass a function as an argument to applymap()the function which returns a value by 10multiplying the input value with . So dfeach element in the DataFrame will be scaled to 10.

We can also use forloop to iterate dfeach element in DataFrame, but it makes our code less readable, messy, and less efficient. applymap()is another alternative that can make the code more readable and efficient.

In addition to mathematical operations, we can also perform other operations on the elements of a DataFrame.

import pandas as pd

df = pd.DataFrame(
    {
        "Col 1": [30, 40, 50, 60],
        "Col 2": [23, 35, 65, 45],
        "Col 3": [85, 87, 90, 89],
    },
    index=["A", "B", "C", "D"],
)

print("Initial DataFrame:")
print(df, "\n")

altered_df = df.applymap(lambda a: str(a) + ".00")

print("Altered DataFrame:")
print(altered_df, "\n")

Output:

Initial DataFrame:
   Col 1  Col 2  Col 3
A     30     23     85
B     40     35     87
C     50     65     90
D     60     45     89

Altered DataFrame:
   Col 1  Col 2  Col 3
A  30.00  23.00  85.00
B  40.00  35.00  87.00
C  50.00  65.00  90.00
D  60.00  45.00  89.00

It dfis added at the end of each element in the DataFrame .00.

map()Methods in Pandas

import pandas as pd

df = pd.DataFrame(
    {
        "Col 1": [30, 40, 50, 60],
        "Col 2": [23, 35, 65, 45],
        "Col 3": [85, 87, 90, 89],
    },
    index=["A", "B", "C", "D"],
)

print("Initial DataFrame:")
print(df, "\n")

df["Col 1"] = df["Col 1"].map(lambda x: x / 100)

print("DataFrame after altering Col 1:")
print(df)

Output:

Initial DataFrame:
   Col 1  Col 2  Col 3
A     30     23     85
B     40     35     87
C     50     65     90
D     60     45     89

DataFrame after altering Col 1:
   Col 1  Col 2  Col 3
A    0.3     23     85
B    0.4     35     87
C    0.5     65     90
D    0.6     45     89

We can use the method only on specific columns of a DataFrame map().

apply()Methods in Pandas

apply()Methods to change the entire DataFrame in Pandas

import pandas as pd

df = pd.DataFrame(
    {
        "Col 1": [30, 40, 50, 60],
        "Col 2": [23, 35, 65, 45],
        "Col 3": [85, 87, 90, 89],
    },
    index=["A", "B", "C", "D"],
)

print("Initial DataFrame:")
print(df, "\n")

altered_df = df.apply(lambda x: x / 100)

print("Altered DataFrame:")
print(altered_df, "\n")

Output:

Initial DataFrame:
   Col 1  Col 2  Col 3
A     30     23     85
B     40     35     87
C     50     65     90
D     60     45     89

Altered DataFrame:
   Col 1  Col 2  Col 3
A    0.3   0.23   0.85
B    0.4   0.35   0.87
C    0.5   0.65   0.90
D    0.6   0.45   0.89

apply()Method to modify only one column in Pandas

import pandas as pd

df = pd.DataFrame(
    {
        "Col 1": [30, 40, 50, 60],
        "Col 2": [23, 35, 65, 45],
        "Col 3": [85, 87, 90, 89],
    },
    index=["A", "B", "C", "D"],
)

print("Initial DataFrame:")
print(df, "\n")

df["Col 1"] = df["Col 1"].apply(lambda x: x / 100)

print("DataFrame after altering Col 1:")
print(df)

Output:

Initial DataFrame:
   Col 1  Col 2  Col 3
A     30     23     85
B     40     35     87
C     50     65     90
D     60     45     89

DataFrame after altering Col 1:
   Col 1  Col 2  Col 3
A    0.3     23     85
B    0.4     35     87
C    0.5     65     90
D    0.6     45     89

So, from the above examples, we can see that apply()methods can be used to apply a particular function to all the elements of the entire DataFrame or all the elements of a particular column.