Python Study notebook1---compare

The explains for both are:

DataFrame.loc:Purely label-location based indexer for selection by label.

.loc[] is primarily label based, but may also be used with a boolean array.

DataFrame.iloc:Purely integer-location based indexing for selection by position.

.iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array.

import pandas as pd

data = [[1,2,3],[4,5,6]]

index = ['a','b']

columns = ['c','d','e']

df = pd.DataFrame(data=data, index=index, columns=columns)

'''

    c  d  e

a  1  2  3

b  4  5  6

'''

print df.loc['a']

print df.iloc[1]

The output:
c 1
d 2
e 3
Name: a, dtype: int64
c 4
d 5
e 6
Name: b, dtype: int64

The parameter i of pa.loc[i] is primarily label of row,the parameter i of pa.iloc[i] is integer positio of row

1. How to use pa.loc

1.1 We can also use pa.loc to get several rows data:

print df.loc['a']

The output is:
c d e
a 1 2 3
b 4 5 6

1.2 Index to raw'a' column 'd'&'e'

print df.loc['a',['d','e']] 

The output is:
d 2
e 3
Name: a, dtype: int64

1.3 Index by column

print df.loc[:,'c']

The output is:
a 1
b 4
Name: c, dtype: int64

2. How to use pa.iloc

2.1 We can also use pa.iloc to get several rows data:

print df.iloc[0:] 

The output is:
c d e
a 1 2 3
b 4 5 6
Name: b, dtype: int64

2.2 Index to first raw second column

print df.iloc[0,[1]] 
c```
The output is:
d    2
Name: a, dtype: int64
#### 1.3 Index by column

print df.iloc[:,[1]]

The output is:
   d
a  2
b  5