Add a new column in Pandas Data Frame Using a Dictionary

Add a new column in Pandas Data Frame Using a Dictionary

Adding a new column to a DataFrame using a dictionary is a common operation in pandas, a popular data manipulation library in Python. Let's go through a step-by-step tutorial.

1. Setting up the environment:

Firstly, you need to have pandas installed:

pip install pandas

Then, import the necessary library:

import pandas as pd

2. Creating a Sample DataFrame:

Let's create a sample DataFrame for demonstration:

df = pd.DataFrame({ 'A': [1, 2, 3], 'B': [4, 5, 6] }) print(df)

This will output:

 A B 0 1 4 1 2 5 2 3 6

3. Adding a New Column using a Dictionary:

Now, let's say we want to add a new column 'C'. We can use a dictionary where keys correspond to the index and values correspond to the data:

new_column_data = { 0: 7, 1: 8, 2: 9 } df['C'] = df.index.map(new_column_data) print(df)

Output:

 A B C 0 1 4 7 1 2 5 8 2 3 6 9

Here, the map() function is used to map the indices of the DataFrame to the values in the dictionary.

4. Considerations:

  • If the dictionary does not have a value for a particular index in the DataFrame, the resulting DataFrame will have NaN (Not a Number) for that index in the new column.

  • If the dictionary has keys that don't correspond to any index in the DataFrame, they will be ignored.

  • The map() method works well for adding a new column based on the DataFrame's index. If you're trying to add a new column based on values from another column, consider using apply() or transform() functions.

Summary:

You can easily add a new column to a pandas DataFrame using a dictionary by leveraging the map() function. The dictionary keys should correspond to the DataFrame's index, and the values will populate the new column.

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