Pandas Array (Pandas Series)-(1)

Import Pandas

import Pandas as PD

Countries = ['Albania','Algeria','Andorra','Angola','Antigua and Barbuda',             'Argentina','Armenia','Australia','Austria','Azerbaijan',             'Bahamas','Bahrain','Bangladesh','Barbados','Belarus',             'Belgium','Belize','Benin','Bhutan','Bolivia']life_expectancy_values= [74.7, 75., 83.4, 57.6, 74.6, 75.4, 72.3, 81.5, 80.2,                          70.3, 72.1, 76.4, 68.1, 75.2, 69.8, 79.4, 70.8, 62.7,                          67.3, 70.6]gdp_values= [1681.61390973, 2155.48523109, 21495.80508273, 562.98768478,              13495.1274663, 9388.68852258, 1424.19056199, 24765.54890176,              27036.48733192, 1945.63754911, 21721.61840978, 13373.21993972,                483.97086804, 9783.98417323, 2253.46411147, 25034.66692293,               3680.91642923, 366.04496652, 1175.92638695, 1132.21387981]

#将普通数组转换为pandas数组

Life_expectancy = PD. Series (life_expectancy_values)

GDP = PD. Series (gdp_values)

Pandas arrays and numpy arrays have many of the same operations:

(1) Interception section

Print Life_expectancy[0]
# results:  print gdp[3:6]
# results:  3      562.9876854    13495.1274665     9388.688523dtype:float64

(2) Cycle:

 for inch life_expectancy: Print ' examining life expectancy {} '. Format (country_life_expectancy)

# results






...

Examining life expectancy 70.6

(3) commonly used functions:

Print Life_expectancy.mean ()   # Find Average
# results

Print life_expectancy.std ()    # ask for standard deviation
# results

Print  # Ask for maximum value
# results

print gdp.sum ()                
# results
182957.59833

(4) Vectorization operations:

A = PD. Series ([1, 2, 3, 4]) b= PD. Series ([1, 2, 1, 2])  PrintA +b#Results0 21 42) 43 6Dtype:int64PrintA * 2#Results0 21 42) 63 8Dtype:int64PrintA >= 3#Results0 False1False2True3Truedtype:boolPrintA[a >= 3]#Results2 33 4Dtype:int64

Pandas Array (Pandas Series)-(1)