How can I “merge” rows in a Pandas DataFrame based on these conditions

Question

My dataFrame looks like this:

+---------------+------+--------+
|     Date      | Type | Number |
+---------------+------+--------+
| 14-March-2020 | A    |     10 |
| 14-March-2020 | B    |     20 |
| 14-March-2020 | C    |     30 |
| 15-March-2020 | A    |     40 |
| 15-March-2020 | B    |     50 |
| 15-March-2020 | C    |     60 |
+---------------+------+--------+

I want to transform it to:

+---------------+----+----+----+
|     Date      | A  | B  | C  |
+---------------+----+----+----+
| 14-March-2020 | 10 | 20 | 30 |
| 15-March-2020 | 40 | 50 | 60 |
+---------------+----+----+----+

I have tried using df.groupby('Date') - for an initial condensation - however that doesn't seem to work. Any help would be great.

Answer 1

A solution that removes also the index 'Type' that remains after pivoting the dataframe involves rename_axis after resetting the index.

import pandas as pd
df.pivot('Date', 'Type', 'Number').reset_index().rename_axis(columns={'Type': ''})

#             Date   A   B   C
# 0  14-March-2020  10  20  30
# 1  15-March-2020  40  50  60

---

If we omit rename_axis , we in fact obtain

df.pivot('Date', 'Type', 'Number').reset_index()
# Type            Date   A   B   C
# 0      14-March-2020  10  20  30
# 1      15-March-2020  40  50  60