Pandas: DataFrame data selection method (index)

Our Series # first create the object, and then merged into dataframe objects inside to 
Import PANDAS AS pd 
Import numpy AS NP

area=pd.Series({'ChongQing':188888,'BeiJing':92387928,'Shanghai':8374583746,'Sydney':82734})
population=pd.Series({'ChongQing':1000,'BeiJing':2000,'Shanghai':2900,'Sydney':3000})
datapd.DataFrame = ({ ' Area ' : Area, ' Population ' : Population}) # NOTE: Be sure to follow the dictionary data structure when creating the dictionary structure 
after # is finished creating the dictionary must be spent in a front-write the dictionary brackets, this is a very important habit 
print (data)

Output:

                 area        population
ChongQing      188888        1000
BeiJing      92387928        2000
Shanghai   8374583746        2900
Sydney          82734        3000

Enter the code on target to increase our colums:

data['area']

Output:

ChongQing        188888
BeiJing        92387928
Shanghai     8374583746
Sydney            82734
Name: area, dtype: int64

Input:

Utilizing # attribute to list a data columns, the above is used in the form of an index, and less common form of such 
data.area

Output:

ChongQing        188888
BeiJing        92387928
Shanghai     8374583746
Sydney            82734
Name: area, dtype: int64

Input:

data.values ​​# dataframe fact is very clear that a two-dimensional array, we can use this formula to verify it

Output:

array([[1.88888000e+05, 1.00000000e+03, 1.88888000e+02],
       [9.23879280e+07, 2.00000000e+03, 4.61939640e+04],
       [8.37458375e+09, 2.90000000e+03, 2.88778750e+06],
       [8.27340000e+04, 3.00000000e+03, 2.75780000e+01]])