text_plain_output_1.png
import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

comp_df = pd.read_csv('../input/free-7-million-company-dataset/companies_sorted.csv')

#Check of dataset
comp_df.head()
comp_df.tail()

comp_df.shape

comp_df.info()
comp_df.describe()

#change name of columns and make it with fist upper letter.
comp_df.rename(columns={'year founded':'year_founded','size range':'size_range','linkedin url':'linkedin_url','current employee estimate':'current_employee_estimate','total employee estimate':'total_employee_estimate'}, inplace=True)
comp_df.columns=comp_df.columns.str.capitalize()
print(comp_df.columns)
comp_df['Country']=comp_df.Country.str.title()
comp_df.head()

continent_df = pd.read_csv('../input/continent/country_continent.csv', delimiter=';', encoding='ISO-8859-1')
continent_1 = continent_df[['Continent', 'Country']]

comp_nonan_df = comp_df.dropna(axis=0, subset=['Country'])
comp_nonan_df.shape
comp_nonan_df.info()
comp_nonan_df.head()
marge_df = pd.merge(comp_nonan_df, continent_1, on='Country', how='left')
marge_df