Compute the cross join between two DataFrames in Pandas
In the following tutorial, we will discuss how to perform a cross join between two Pandas DataFrames.
Steps to calculate a cross join between two DataFrames in Pandas
Following are the steps to calculate the cross join between two DataFrames in Pandas.
Import Pandas
We will import the Pandas library to perform a cross join to get started.
import pandas as pd
Creating a Pandas DataFrame
We will now create two sample DataFrames to perform the cross join operation. These two DataFrames will contain letters and numbers respectively.
data1 = {"A": ["a", "b"]}
data2 = {"B": [1, 2, 3]}
df = pd.DataFrame(data1, index=[0, 1])
df1 = pd.DataFrame(data2, index=[2, 3, 4])
Compute the cross join between two DataFrames in Pandas
To perform a cross join between the two created example DataFrames, we need to create a key column in both DataFrames to merge on the same key column.
df["key"] = 2
df1["key"] = 2
We will merge keythe two DataFrames on the new column and drop keythe column to perform a cross join.
res = pd.merge(df, df1, on="key").drop("key", axis=1)
Now, print resthe variable to see the result of the cross join between our two DataFrames.
Output:
A B
0 a 1
1 a 2
2 a 3
3 b 1
4 b 2
5 b 3
We can see the cross join between the two sample DataFrames in the output. Hence, we can successfully calculate the cross join between two DataFrames in Pandas using the above technique.
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