I have one massive pandas dataframe with this structure:
df1:
A B
0 0 12
1 0 15
2 0 17
3 0 18
4 1 45
5 1 78
6 1 96
7 1 32
8 2 45
9 2 78
10 2 44
11 2 10
And a second one, smaller like this:
df2
G H
0 0 15
1 1 45
2 2 31
I want to add a column to my first dataframe following this rule: column df1.C = df2.H when df1.A == df2.G
I manage to do it with for loops, but the database is massive and the code run really slowly so I am looking for a Pandas-way or numpy to do it.
Many thanks,
Boris
The compare method in pandas shows the differences between two DataFrames. It compares two data frames, row-wise and column-wise, and presents the differences side by side. The compare method can only compare DataFrames of the same shape, with exact dimensions and identical row and column labels.
DataFrame - equals() function The equals() function is used to test whether two objects contain the same elements. This function allows two Series or DataFrames to be compared against each other to see if they have the same shape and elements. NaNs in the same location are considered equal.
Since your goal is just to compare differences, use DataFrame. compare instead of aggregating into strings. So we just need to align the row/column indexes, either via merge or reindex .
If you only want to match mutual rows in both dataframes:
import pandas as pd
df1 = pd.DataFrame({'Name':['Sara'],'Special ability':['Walk on water']})
df1
Name Special ability
0 Sara Walk on water
df2 = pd.DataFrame({'Name':['Sara', 'Gustaf', 'Patrik'],'Age':[4,12,11]})
df2
Name Age
0 Sara 4
1 Gustaf 12
2 Patrik 11
df = df2.merge(o, left_on='Name', right_on='Name', how='left')
df
Name Age Special ability
0 Sara 4 NaN
1 Gustaf 12 Walk on water
2 Patrik 11 NaN
This Can allso be done with more than one matching argument: (In this example Patrik from df1 does not exist in df2 becuse they have different ages and therfore will not merge)
df1 = pd.DataFrame({'Name':['Sara','Patrik'],'Special ability':['Walk on water','FireBalls'],'Age':[12,83]})
df1
Name Special ability Age
0 Sara Walk on water 12
1 Patrik FireBalls 83
df2 = pd.DataFrame({'Name':['Sara', 'Gustaf', 'Patrik'],'Age':[4,12,11]})
df2
Name Age
0 Sara 4
1 Gustaf 12
2 Patrik 11
df = df2.merge(df1,left_on=['Name','Age'],right_on=['Name','Age'],how='left')
df
Name Age Special ability
0 Sara 12 Walk on water
1 Gustaf 12 NaN
2 Patrik 11 NaN
You probably want to use a merge:
df=df1.merge(df2,left_on="A",right_on="G")
will give you a dataframe with 3 columns, but the third one's name will be H
df.columns=["A","B","C"]
will then give you the column names you want
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