In-place sort_values in pandas what does it exactly mean?

Here an example. df1 will hold sorted dataframe and df will be intact

import pandas as pdfrom datetime import datetime as dtdf = pd.DataFrame(data=[22,22,3],                  index=[dt(2016, 11, 10, 0), dt(2016, 11, 10, 13), dt(2016, 11, 13, 5)],                  columns=['foo'])df1 = df.sort_values(by='foo')print(df, df1)

In the case below, df will hold sorted values

import pandas as pdfrom datetime import datetime as dtdf = pd.DataFrame(data=[22,22,3],                  index=[dt(2016, 11, 10, 0), dt(2016, 11, 10, 13), dt(2016, 11, 13, 5)],                  columns=['foo'])df.sort_values(by='foo', inplace=True)print(df)

As you can read from the sort_values document, the return value of the function is a series. However, it is a new series instead of the original.

For example:

import numpy as npimport pandas as pds = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])print(s)a   -0.872271b    0.294317c   -0.017433d   -1.375316e    0.993197dtype: float64s_sorted = s.sort_values()print(s_sorted)d   -1.375316a   -0.872271c   -0.017433b    0.294317e    0.993197dtype: float64print(id(s_sorted))127952880print(id(s))127724792

So s and s_sorted are different series. But if you use inplace=True.

s.sort_values(inplace=True)print(s)d   -1.375316a   -0.872271c   -0.017433b    0.294317e    0.993197dtype: float64print(id(s))127724792

It shows they are the same series, and no new series will return.

"inplace=True" is more like a physical sort while "inplace=False" is more like logic sort. The physical sort means that the data sets saved in the computer is sorted based on some keys; and the logic sort means the data sets saved in the computer is still saved in the original (when it was input/imported) way, and the sort is only working on the their index. A data sets have one or multiple logic index, but physical index is unique.