How to add new column sample code to an existing DataFrame

Basic Principles

In Python, using the Pandas library makes it very convenient to process data. DataFrame is the main data structure used in the Pandas library to store table data, similar to tables in Excel. Sometimes, we may need to add new columns to an existing DataFrame. This can be achieved in a number of ways, including using the values of an existing column, creating a column that is all zero or all one, or directly adding a column consisting of a specific value.

Code Example

Example 1: Create a new column with the value of an existing column

Suppose we have a DataFrame, now we want to create a new column based on the existing column. For example, we have a namedfDataFrame, which containsAandBTwo columns, we want to create a new columnC, its value isAColumn andBcolumn sum.

import pandas as pd

# Create a sample DataFramedf = ({
    'A': [1, 2, 3],
    'B': [4, 5, 6]
})

# Create a new column C with the sum of column A and column Bdf['C'] = df['A'] + df['B']

print(df)

Example 2: Add a column with all zeros

If we want to add a new column, all of its values are initialized to zero, we can do this:

# Add a new column with all zeros Ddf['D'] = 0

print(df)

Example 3: Add a column with all specific values

Sometimes, we may need to add a new column, all of which are a specific value, such as a constant or a specific string.

# Add a new column E with all specific valuesdf['E'] = 'constant_value'

print(df)

Example 4: Add a new column using the apply function

We can also useapplyFunction to apply a function to each row of the DataFrame, thereby creating a new column.

# Use the apply function to add a new column F, which is the product of column A and column Bdf['F'] = (lambda row: row['A'] * row['B'], axis=1)

print(df)

Things to note

Data type consistency: When adding a new column, make sure that the data type of the new column is compatible with the data types of other columns in the DataFrame.
Index alignment: When creating a new column based on an existing column, make sure that all rows are indexed to avoid NaN values.
Memory usage: When adding a large number of columns or large data sets, pay attention to memory usage.
Performance considerations: For large DataFrames, it may take some time to add new columns, especially when using complex functions or operations.

in conclusion

Adding new columns to a DataFrame is a common operation in data processing. Pandas provides a variety of flexible ways to implement this functionality. Understanding these methods and choosing the appropriate method according to specific needs can greatly improve the efficiency and flexibility of data processing. Through practice and exploration, we can better grasp the power of the Pandas library, so as to process and analyze data more efficiently.

Summarize

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