Sometimes, we wanna couple multiple dataframes together. In this note, I use df as DataFrame, s as Series.

Libraries

import pandas as pd
import numpy as np

Coupling dfs with merge()

There are 4 types of merging, like in SQL.

• Inner: only includes elements that appear in both dataframes with a common key.
• Outer: includes all data from both dataframes.
• Left: includes all of the rows from the "left" dataframe along with any rows from the "right" dataframe with a common key; the result retains all columns from both of the original dataframes.
• Right: includes all of the rows from the "right" dataframe along with any rows from the "left" dataframe with a common key; the result retains all columns from both of the original dataframes.

On the same column name,

# left
df_left = pd.merge(left=df1, right=df2, how='left', on='Col_1', suffixes=('_df1', '_df2'))
# right
df_right = pd.merge(left=df1, right=df2, how='right', on='Col_1', suffixes=('_df1', '_df2'))

# inner (defaut)
df_inner = pd.merge(left=df1, right=df2, on='Col_1', suffixes=('_df1', '_df2'))
# outer
df_outer = pd.merge(left=df1, right=df2, how='outer', on='Col_1', suffixes=('_df1', '_df2'))

display_side_by_side(df1, df2, df_inner, df_outer)

On the different column names,

# left
df_left = pd.merge(left=df1, right=df2, how='left', left_on='Col_1', right_on='Col_X', suffixes=('_df1', '_df2'))

display_side_by_side(df1, df2, df_left)