How to map values in Pandas DataFrame in Python
Mapping values in a Pandas DataFrame is a common data wrangling task. One way to do this is by using the map() function.
The map() function is a built-in function in Python that applies a given function to all items in an input list, tuple or other iterable, and returns a new list containing the results. In the context of a Pandas DataFrame, the map() function can be used to apply a function to one or more columns of the DataFrame.
Here’s an example of how to use the map() function to map values in a Pandas DataFrame:
import pandas as pd data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)
df['A'] = df['A'].map(square)map() function is used to apply the square() function to the values in column ‘A’ of the DataFrame. The result is a new DataFrame with the values in column ‘A’ squared.It’s also possible to use map with a dictionary. In this case it will map the keys of the dictionary to the corresponding values.
data = {'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}
df = pd.DataFrame(data)
mapping = {1: 'One', 2: 'Two', 3: 'Three'}
df["A"] = df["A"].map(mapping)
In this way, you can map the values of any column in a DataFrame using the map() function. It can be a powerful tool when working with large datasets.
Additionally, Pandas also provides replace() function that can be use to replace values in the DataFrame. This function can take a dictionary to replace multiple values at once or a single value or even a list of values. It can also replace the values based on a condition by passing inplace=True or inplace=False and is more efficient when you have large DataFrame.
In this Learn through Codes example, you will learn: How to map values in Pandas DataFrame in Python.
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