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