Concatenate

Learn how to apply the concatenate() method to combine data in pandas.

Concept of concatenate

The term concatenate is defined as the action of linking or stitching objects together in a chain. In the context of pandas, we concatenate different pandas objects together, e.g., a DataFrame with another DataFrame. The type of concatenation we can perform on the objects depends on the axis of the linkage, i.e., either row-wise or column-wise.

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Illustration of row-wise and column-wise axes
Illustration of row-wise and column-wise axes

For this lesson, we’ll use a mock dataset of cars insured by a motor insurance company to demonstrate how concatenation works.

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# Read data from CSV file

df = pd.read_csv('../usr/local/data/csv/insured_cars.csv')



# Display 10 sample rows

print(df.sample(10))

Row-wise concatenation

In row-wise concatenation, we’re linking two pandas objects on top of one another (i.e., vertically).

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Illustration of row-wise concatenation
Illustration of row-wise concatenation

Let’s say we have two DataFrames df_A and df_B, where df_A comprises data on cars with a model year before 2005 (inclusive). On the other hand, df_B comprises data on cars with a model year after 2006 (inclusive).

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# Display heads of DataFrames A and B
print(df_A.sort_values(by='car_model_year').tail())
# Print line separator to better visualize different outputs
print('=' * 65)
print(df_B.sort_values(by='car_model_year').head())

To create a complete dataset (from df_A and df_B) with all the car model years present, the ...

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