How to select a list of rows by name in Pandas dataframe

You can use df.loc[['TP3','TP12','TP18']]

Here is a small example:

In [26]: df = pd.DataFrame({"a": [1,2,3], "b": [3,4,5], "c": [5,6,7]})In [27]: df.index = ["x", "y", "z"]In [28]: dfOut[28]:    a  b  cx  1  3  5y  2  4  6z  3  5  7[3 rows x 3 columns]In [29]: df.loc[["x", "y"]]Out[29]:    a  b  cx  1  3  5y  2  4  6[2 rows x 3 columns]

You can select the rows by position:

df.iloc[[0,2,4], :]

There are at least 3 ways to access the element of of a pandas dataframe.

import pandas as pdimport numpy as npdf=pd.DataFrame(np.random.uniform(size=(10,10)),columns= list('PQRSTUVWXY'),index= list("ABCDEFGHIJ"))

Using df[['P','Q']] you can only access the columns of the dataframe. You can use the dataframe.loc[] (stands for location) or dataframe.iloc[] (stands for index location) numpy style slicing of the dataframe.

df.loc[:,['P','Q']]

Above will give you columns named by 'P' and 'Q'.

df.loc[['A','B'],:]

Above will return rows with keys 'A' and 'B'.

You can also use number based slicing using iloc method.

df.iloc[:,[1,2]]

This will return columns numbered by 1 and 2.While,

df.iloc[[1,2],:]

will return rows 1st and 2nd.You can access any specific element by

df.iloc[1,2]

or,

df.loc['A','Q']