How to concatenate in Pandas

How to concatenate in Pandas

To concatenate two or more Pandas DataFrames, you can use the .concat() method.

This method will concatenate the rows or columns of the given DataFrames, depending on the value of the axis parameter.

Examples

Here are a few examples:

import pandas as pd

# Create two sample Pandas DataFrames
df1 = pd.DataFrame({'fruit': ['apple', 'orange', 'apple', 'banana'],
                    'color': ['red', 'orange', 'green', 'yellow']})

df2 = pd.DataFrame({'fruit': ['pineapple', 'strawberry', 'banana'],
                    'color': ['yellow', 'red', 'brown']})

# Use the concat() method to concatenate the DataFrames by rows
df = pd.concat([df1, df2], axis=0)

# Print the concatenated DataFrame
print(df)

This will output the following DataFrame, which contains the rows from both of the input DataFrames:

        fruit   color
0       apple     red
1      orange  orange
2       apple   green
3      banana  yellow
0   pineapple  yellow
1  strawberry     red
2      banana   brown

You can also concatenate the DataFrames by columns, using the axis=1 parameter.

For example:

# Use the concat() method to concatenate the DataFrames by columns
df = pd.concat([df1, df2], axis=1)

# Print the concatenated DataFrame
print(df)

This will output the following DataFrame, which contains the columns from both of the input DataFrames:

    fruit   color       fruit   color
0   apple     red   pineapple  yellow
1  orange  orange  strawberry     red
2   apple   green      banana   brown
3  banana  yellow         NaN     NaN

In all of these examples, df1 and df2 are the names of the input DataFrames, and df is the name of the concatenated DataFrame.

You can replace these names with the names of your own DataFrames if they are different.