In [15]:

import pandas as pd

Get a suitable data set and put it in a DataFrame.

In [16]:

from sklearn.datasets import load_boston
boston = load_boston()
df = pd.DataFrame(boston.data, columns=boston['feature_names'])

Add a dummy index column.

In [17]:

df.insert(0, 'ID', 100000 + df.index)

In [13]:

df.head(10)

Out[13]:

	ID	CRIM	ZN	INDUS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT
0	100000	0.00632	18.0	2.31	0.538	6.575	65.2	4.0900	1.0	296.0	15.3	396.90	4.98
1	100001	0.02731	0.0	7.07	0.469	6.421	78.9	4.9671	2.0	242.0	17.8	396.90	9.14
2	100002	0.02729	0.0	7.07	0.469	7.185	61.1	4.9671	2.0	242.0	17.8	392.83	4.03
3	100003	0.03237	0.0	2.18	0.458	6.998	45.8	6.0622	3.0	222.0	18.7	394.63	2.94
4	100004	0.06905	0.0	2.18	0.458	7.147	54.2	6.0622	3.0	222.0	18.7	396.90	5.33
5	100005	0.02985	0.0	2.18	0.458	6.430	58.7	6.0622	3.0	222.0	18.7	394.12	5.21
6	100006	0.08829	12.5	7.87	0.524	6.012	66.6	5.5605	5.0	311.0	15.2	395.60	12.43
7	100007	0.14455	12.5	7.87	0.524	6.172	96.1	5.9505	5.0	311.0	15.2	396.90	19.15
8	100008	0.21124	12.5	7.87	0.524	5.631	100.0	6.0821	5.0	311.0	15.2	386.63	29.93
9	100009	0.17004	12.5	7.87	0.524	6.004	85.9	6.5921	5.0	311.0	15.2	386.71	17.10

Another way to do it, is to reset the index.

In [18]:

df = pd.DataFrame(boston.data, columns=boston['feature_names'])

df = df.reset_index()

In [19]:

df.head(10)

Out[19]:

	index	CRIM	ZN	INDUS	NOX	RM	AGE	DIS	RAD	TAX	PTRATIO	B	LSTAT
0	0	0.00632	18.0	2.31	0.538	6.575	65.2	4.0900	1.0	296.0	15.3	396.90	4.98
1	1	0.02731	0.0	7.07	0.469	6.421	78.9	4.9671	2.0	242.0	17.8	396.90	9.14
2	2	0.02729	0.0	7.07	0.469	7.185	61.1	4.9671	2.0	242.0	17.8	392.83	4.03
3	3	0.03237	0.0	2.18	0.458	6.998	45.8	6.0622	3.0	222.0	18.7	394.63	2.94
4	4	0.06905	0.0	2.18	0.458	7.147	54.2	6.0622	3.0	222.0	18.7	396.90	5.33
5	5	0.02985	0.0	2.18	0.458	6.430	58.7	6.0622	3.0	222.0	18.7	394.12	5.21
6	6	0.08829	12.5	7.87	0.524	6.012	66.6	5.5605	5.0	311.0	15.2	395.60	12.43
7	7	0.14455	12.5	7.87	0.524	6.172	96.1	5.9505	5.0	311.0	15.2	396.90	19.15
8	8	0.21124	12.5	7.87	0.524	5.631	100.0	6.0821	5.0	311.0	15.2	386.63	29.93
9	9	0.17004	12.5	7.87	0.524	6.004	85.9	6.5921	5.0	311.0	15.2	386.71	17.10

Make a dummy index column