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In [1]:
import numpy as np
import pandas as pd

from pandas import Series, DataFrame
Selecting and retrieving data
In [2]:
series_obj = Series(np.arange(8),index=['row 1','row 2','row 3','row 4','row 5','row 6','row 7','row 8'])
series_obj
Out[2]:
row 1    0
row 2    1
row 3    2
row 4    3
row 5    4
row 6    5
row 7    6
row 8    7
dtype: int64
In [3]:
series_obj['row 7']
Out[3]:
6
In [4]:
series_obj[[0,7]]
Out[4]:
row 1    0
row 8    7
dtype: int64
In [5]:
np.random.seed(25)
Df_obj = DataFrame(np.random.rand(36).reshape((6,6)), index=['row 1','row 2','row 3','row 4','row 5','row 6'],
                                              columns = ['column 1','column 2','column 3','column 4','column 5','column 6'])
Df_obj
Out[5]:
column 1	column 2	column 3	column 4	column 5	column 6
row 1	0.870124	0.582277	0.278839	0.185911	0.411100	0.117376
row 2	0.684969	0.437611	0.556229	0.367080	0.402366	0.113041
row 3	0.447031	0.585445	0.161985	0.520719	0.326051	0.699186
row 4	0.366395	0.836375	0.481343	0.516502	0.383048	0.997541
row 5	0.514244	0.559053	0.034450	0.719930	0.421004	0.436935
row 6	0.281701	0.900274	0.669612	0.456069	0.289804	0.525819
In [6]:
Df_obj.loc[['row 2', 'row 5'], ['column 5', 'column 2']]
Out[6]:
column 5	column 2
row 2	0.402366	0.437611
row 5	0.421004	0.559053
Data Slicing
In [7]:
series_obj['row 2' : 'row 7']
Out[7]:
row 2    1
row 3    2
row 4    3
row 5    4
row 6    5
row 7    6
dtype: int64
Data comparison
In [10]:
Df_obj< 0.2
Out[10]:
column 1	column 2	column 3	column 4	column 5	column 6
row 1	False	False	False	True	False	True
row 2	False	False	False	False	False	True
row 3	False	False	True	False	False	False
row 4	False	False	False	False	False	False
row 5	False	False	True	False	False	False
row 6	False	False	False	False	False	False
Filtering with scalars
In [38]:
series_obj[series_obj<6]
Out[38]:
row 1    0
row 2    1
row 3    2
row 4    3
row 5    4
row 6    5
dtype: int64
Setting values with scalars
In [39]:
series_obj['row 1', 'row 2', 'row 3'] = 8
In [40]:
series_obj
Out[40]:
row 1    8
row 2    8
row 3    8
row 4    3
row 5    4
row 6    5
row 7    6
row 8    7
dtype: int64
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