Sometimes, we wanna couple multiple dataframes together. In this note, I use
df as DataFrame, s as Series.1import pandas as pd
2import numpy as npThere 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
1# left
2df_left = pd.merge(left=df1, right=df2, how='left', on='Col_1', suffixes=('_df1', '_df2'))
3# right
4df_right = pd.merge(left=df1, right=df2, how='right', on='Col_1', suffixes=('_df1', '_df2'))1# inner (defaut)
2df_inner = pd.merge(left=df1, right=df2, on='Col_1', suffixes=('_df1', '_df2'))
3# outer
4df_outer = pd.merge(left=df1, right=df2, how='outer', on='Col_1', suffixes=('_df1', '_df2'))
5
6display_side_by_side(df1, df2, df_inner, df_outer)On the different column names
1# left
2df_left = pd.merge(left=df1, right=df2, how='left', left_on='Col_1', right_on='Col_X', suffixes=('_df1', '_df2'))
3
4display_side_by_side(df1, df2, df_left)The result keeps both
Col_1 and Col_X while in the case of the same column name, there is only 1 column. Other words, in this case, we only want to keep Col_1 and don't need Col_X. How to do that?1df_left = df1.set_index('Col_1').join(df2.set_index('Col_X'), how="left", lsuffix="_df1", rsuffix="_df2").reset_index()
2
3display_side_by_side(df1, df2, df_left)1# axis=0 (default)
2df_concat_0 = pd.concat([df1, df2]) # the same columns
3df_concat_1 = pd.concat([df1, df2], axis=1) # the same rows
4
5df_concat_0_idx = pd.concat([df1, df2], ignore_index=True)
6# ignore_index=True prevent duplicating indexes
7
8display_side_by_side(df1, df2)
9display_side_by_side(df_concat_0, df_concat_1, df_concat_0_idx)We consider a situation in that we need to combine 2 dfs containing missing values in each. The missing values will be filled by taking from the others. For example, the value of
C in the left df can be fulfilled by the value of in the right df.1df_comb = df1.copy() # we don't want to change df1
2df_new = df_comb.fillna(df2)
3
4display_side_by_side(df1, df2, df_comb, df_new)