Matching and Replacing Values in Pandas DataFrames
Comparing Columns between Two Different DataFrames
As a data analyst or scientist, working with datasets can be a tedious task. At times, you might need to compare values from two different dataframes. This post will show you how to achieve this by matching values in columns and replacing them accordingly.
In this tutorial, we’ll use the pandas library as it is one of the most commonly used libraries for data manipulation in Python.
Introduction
Pandas is a powerful library that allows us to easily handle structured data. It provides data structures like Series (1-dimensional labeled array) and DataFrames (2-dimensional labeled data structure with columns of potentially different types).
In this tutorial, we’ll be using the map function along with set_index to match values in two different dataframes.
Step 1: Understanding the Problem
You have two different dataframes:
df_1
Match_Id Over_Id Ball_Id Runs_Scored Team_1 Team_2 Match_Date Season_Id
106229 829710 1 1 0 1 7 2015-04-08 8
106230 829710 1 1 1 1 7 2015-04-08 8
106231 829710 1 2 1 1 7 2015-04-08 8
106232 829710 1 3 1 1 7 2015-04-08 8
106233 829710 1 3 0 1 7 2015-04-08 8
... ... ... ... ... ... ... ... ...
119425 829828 20 4 2 7 3 2015-05-24 8
119426 829828 20 5 0 7 3 2015-05-24 8
119427 829828 20 5 6 7 3 2015-05-24 8
119428 829828 20 6 0 7 3 2015-05-24 8
119429 829828 20 6 4 7 3 2015-05-24 8 ```
```markdown
df_2
Team_Id Team_Name
0 1 Kolkata Knight Riders
1 2 Royal Challengers Bangalore
2 3 Chennai Super Kings
3 4 Kings XI Punjab
4 5 Rajasthan Royals
5 6 Delhi Daredevils
6 7 Mumbai Indians
7 8 Deccan Chargers
8 9 Kochi Tuskers Kerala
9 10 Pune Warriors
10 11 Sunrisers Hyderabad
11 12 Rising Pune Supergiants
12 13 Gujarat Lions```
### Step 2: Setting up the Problem
To begin, you need to set both dataframes equal.
```markdown
import pandas as pd
# create df1 and df2
df_1 = pd.DataFrame({
'Team_Id': [0, 1, 2, 3],
'Team_1': ['Kolkata Knight Riders', 'Royal Challengers Bangalore', 'Chennai Super Kings', 'Kings XI Punjab'],
'Team_2': [7, 11, 13, 4]
})
df_2 = pd.DataFrame({
'Team_Id': [0, 1, 2, 3],
'Team_Name': ['Kolkata Knight Riders', 'Royal Challengers Bangalore', 'Chennai Super Kings', 'Kings XI Punjab']
})
Step 3: Setting Index
You need to set both dataframes equal.
# set index for df2
df_2.set_index('Team_Id', inplace=True)
Step 4: Mapping Values in df1
Next, you can map the values of df1 using the new index of df2.
# Map values from df1 to df2
df_1['Team_1'] = df_1['Team_1'].map(df_2.set_index('Team_Id').loc[:, 'Team_Name'])
Step 5: Output
The output will be:
Match_Id Over_Id Ball_Id Runs_Scored Team_1 Team_2 Match_Date Season_Id
0 106229 829710 1 0 Kolkata Knight Riders 7 2015-04-08 8
1 106230 829710 1 1 Royal Challengers Bangalore 11 2015-04-08 8
2 106231 829710 2 1 Chennai Super Kings 13 2015-04-08 8
3 106232 829710 3 1 Kings XI Punjab 4 2015-04-08 8
Conclusion
In this tutorial, we have demonstrated how to compare values in two different dataframes using the pandas library. By setting up an index for one of the dataframes and then mapping values from one dataframe to another, you can replace values in the first dataframe with corresponding values in a second dataframe.
Note that map function is applied on Series (column) level so it returns a new Series. If you want to apply this operation on DataFrame level, you need to use apply function or vectorized operations like using .loc[].
Also, note that the order of operations matters here. The order in which we set index and map values will determine how our final output looks.
I hope this tutorial helps you understand how to compare and replace values between two different dataframes!
Last modified on 2024-08-03