Why does transposing a DataFrame with strings and timedeltas convert the dtype?

Question

This behavior seems odd to me: the id column (a string) gets converted to a timestamp upon transposing the df if the other column is a timedelta.

import pandas as pd
df = pd.DataFrame({'id': ['00115', '01222', '32333'],
                   'val': [12, 14, 170]})
df['val'] = pd.to_timedelta(df.val, unit='Minutes')

print(df.T)
#                         0                      1                      2
#id  0 days 00:00:00.000000 0 days 00:00:00.000001 0 days 00:00:00.000032
#val      365 days 05:49:12      426 days 02:47:24     5174 days 06:27:00

type(df.T[0][0])
#pandas._libs.tslib.Timedelta

Without the timedelta it works as I'd expect, and the id column remains a string, even though the other column is an integer and all of the strings could be safely cast to integers.

df2 = pd.DataFrame({'id': ['00115', '01222', '32333'],
                    'val': [1, 1231, 1413]})

type(df2.T[0][0])
#str

Why does the type of id get changed in the first instance, but not the second?

Answer 1

A dataframe should be thought of in columns. Each column must have a single data type. When you transpose, you are changing which cells are now associated with each other in the new columns. Prior to transpose, you had an string column and a timedelta column. After transpose, each column had a string and a timedelta. Pandas has to decide how to cast the new columns. It decided to go with timedelta. It is my opinion that this is a goofy choice.

You can change this behavior by changing the dtype on a newly constructed dataframe.

pd.DataFrame(df.values.T, df.columns, df.index, dtype=object)

                     0                  1                   2
id               00115              01222               32333
val  365 days 05:49:12  426 days 02:47:24  5174 days 06:27:00