In [2]:

    

import pandas as pd
df = pd.read_csv('../datasets/UN.csv')
print('----')
# print the raw column information plus summary header
print(df)
print('----')
# look at the types of each column explicitly
print('Individual columns - Python data types')
[(x, type(df[x][0])) for x in df.columns]


    

    




----
<class 'pandas.core.frame.DataFrame'>
Int64Index: 207 entries, 0 to 206
Data columns (total 14 columns):
country                   207  non-null values
region                    207  non-null values
tfr                       197  non-null values
contraception             144  non-null values
educationMale             76  non-null values
educationFemale           76  non-null values
lifeMale                  196  non-null values
lifeFemale                196  non-null values
infantMortality           201  non-null values
GDPperCapita              197  non-null values
economicActivityMale      165  non-null values
economicActivityFemale    165  non-null values
illiteracyMale            160  non-null values
illiteracyFemale          160  non-null values
dtypes: float64(12), object(2)
----
Individual columns - Python data types






    
Out[2]:





[('country', str),
 ('region', str),
 ('tfr', numpy.float64),
 ('contraception', numpy.float64),
 ('educationMale', numpy.float64),
 ('educationFemale', numpy.float64),
 ('lifeMale', numpy.float64),
 ('lifeFemale', numpy.float64),
 ('infantMortality', numpy.float64),
 ('GDPperCapita', numpy.float64),
 ('economicActivityMale', numpy.float64),
 ('economicActivityFemale', numpy.float64),
 ('illiteracyMale', numpy.float64),
 ('illiteracyFemale', numpy.float64)]

In [2]:

    

from IPython.core.display import HTML
def css_styling():
    styles = open("../styles/custom.css", "r").read()
    return HTML(styles)
css_styling()


    

    
Out[2]: