import pandas as pd
import os
print(os.getcwd())
C:\Users\Juvette
stats = pd.read_csv('D:\\OneDrive - office365hubs.com\\.Python for data science\\Demographic-Data.csv')
stats
| Country Name | Country Code | Birth rate | Internet users | Income Group | |
|---|---|---|---|---|---|
| 0 | Aruba | ABW | 10.244 | 78.9 | High income |
| 1 | Afghanistan | AFG | 35.253 | 5.9 | Low income |
| 2 | Angola | AGO | 45.985 | 19.1 | Upper middle income |
| 3 | Albania | ALB | 12.877 | 57.2 | Upper middle income |
| 4 | United Arab Emirates | ARE | 11.044 | 88.0 | High income |
| ... | ... | ... | ... | ... | ... |
| 190 | Yemen, Rep. | YEM | 32.947 | 20.0 | Lower middle income |
| 191 | South Africa | ZAF | 20.850 | 46.5 | Upper middle income |
| 192 | Congo, Dem. Rep. | COD | 42.394 | 2.2 | Low income |
| 193 | Zambia | ZMB | 40.471 | 15.4 | Lower middle income |
| 194 | Zimbabwe | ZWE | 35.715 | 18.5 | Low income |
195 rows × 5 columns
stats
| Country Name | Country Code | Birth rate | Internet users | Income Group | |
|---|---|---|---|---|---|
| 0 | Aruba | ABW | 10.244 | 78.9 | High income |
| 1 | Afghanistan | AFG | 35.253 | 5.9 | Low income |
| 2 | Angola | AGO | 45.985 | 19.1 | Upper middle income |
| 3 | Albania | ALB | 12.877 | 57.2 | Upper middle income |
| 4 | United Arab Emirates | ARE | 11.044 | 88.0 | High income |
| ... | ... | ... | ... | ... | ... |
| 190 | Yemen, Rep. | YEM | 32.947 | 20.0 | Lower middle income |
| 191 | South Africa | ZAF | 20.850 | 46.5 | Upper middle income |
| 192 | Congo, Dem. Rep. | COD | 42.394 | 2.2 | Low income |
| 193 | Zambia | ZMB | 40.471 | 15.4 | Lower middle income |
| 194 | Zimbabwe | ZWE | 35.715 | 18.5 | Low income |
195 rows × 5 columns
#Number of rows
len(stats) #195 rows imported
195
# see the columns
stats.columns
Index(['Country Name', 'Country Code', 'Birth rate', 'Internet users',
'Income Group'],
dtype='object')
len(stats.columns)
5
stats.