﻿ Pandas: Split a given dataframe into groups and display target column as a list of unique values - w3resource # Pandas: Split a given dataframe into groups and display target column as a list of unique values

## Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-20 with Solution

Write a Pandas program to split a given dataframe into groups and display target column as a list of unique values.

Test Data:

```   id  type     book
0   A     1     Math
1   A     1     Math
2   A     1  English
3   A     1  Physics
4   A     2     Math
5   A     2  English
6   B     1  Physics
7   B     1  English
8   B     1  Physics
9   B     2  English
10  B     2  English
```

Sample Solution:

Python Code :

``````import pandas as pd
df = pd.DataFrame( {'id' : ['A','A','A','A','A','A','B','B','B','B','B'],
'type' : [1,1,1,1,2,2,1,1,1,2,2],
'book' : ['Math','Math','English','Physics','Math','English','Physics','English','Physics','English','English']})

print("Original DataFrame:")
print(df)
new_df = df[['id', 'type', 'book']].drop_duplicates()\
.groupby(['id','type'])['book']\
.apply(list)\
.reset_index()

new_df['book'] = new_df.apply(lambda x: (','.join([str(s) for s in x['book']])), axis = 1)
print("\nList all unique values in a group:")
print(new_df)
``````

Sample Output:

```Original DataFrame:
id  type     book
0   A     1     Math
1   A     1     Math
2   A     1  English
3   A     1  Physics
4   A     2     Math
5   A     2  English
6   B     1  Physics
7   B     1  English
8   B     1  Physics
9   B     2  English
10  B     2  English

List all unique values in a group:
id  type                  book
0  A     1  Math,English,Physics
1  A     2          Math,English
2  B     1       Physics,English
3  B     2               English
```

Python Code Editor:

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## Python: Tips of the Day

Negative Indexing:

In Python you can use negative indexing. While positive index starts with 0, negative index starts with -1.

```name="Welcome"
print(name)
print(name[-1])
print(name[0:3])
print(name[-1:-4:-1])

```

Output:

```W
e
Wel
emo```