<jupyter_start><jupyter_text><jupyter_code>from google.colab import files
uploaded = files.upload()
!mkdir data

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np<jupyter_output><empty_output><jupyter_text>**Importing World Female LFPR Cleaned Dataset**<jupyter_code>data = "4. Female LFPR - Cleaning.csv"
FemaleLFPR = pd.read_csv(data)
FemaleLFPR.head()
from google.colab import files
uploaded = files.upload()
data2 = "Africa_FULL_MERGE.csv"
FemaleLFPR_Africa = pd.read_csv(data2)
FemaleLFPR_Africa.head()<jupyter_output><empty_output><jupyter_text>**Creating Dataframe with only African countries**<jupyter_code>FemaleLFPR_Africa = FemaleLFPR_Africa[['Country', 'Year', 'Region', 'Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)']]
FemaleLFPR_Africa.head()
FemaleLFPR_Africa = FemaleLFPR_Africa[FemaleLFPR_Africa['Year'] <= 2017]
FemaleLFPR_Africa = FemaleLFPR_Africa[FemaleLFPR_Africa['Year'] >= 1990]
FemaleLFPR_Africa.head()
FemaleLFPR_Africa.reset_index(drop=True)

FemaleLFPR_Africa.to_csv('Female LFPR Africa.csv')<jupyter_output><empty_output><jupyter_text>**World Statistics**<jupyter_code>FemaleLFPR.describe()
#world statistics<jupyter_output><empty_output><jupyter_text>**Africa Statistics**<jupyter_code>FemaleLFPR_Africa.describe()
#Africa statistics
# Mean for African countries is higher compared to the World average 
# Max is equal, meaning that the maximum of female LFPR is in an African country<jupyter_output><empty_output><jupyter_text>**Niger Statistics**<jupyter_code>FemaleLFPR_Niger = FemaleLFPR[FemaleLFPR['Entity'] == 'Niger']
FemaleLFPR_Niger = FemaleLFPR_Niger.reset_index(drop=True)
FemaleLFPR_Niger = FemaleLFPR_Niger[['Entity', 'Code', 'Year', 'Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)']]
FemaleLFPR_Niger
FemaleLFPR_Niger.describe()<jupyter_output><empty_output><jupyter_text>**Tunisia Statistics**<jupyter_code>FemaleLFPR_Tunisia = FemaleLFPR[FemaleLFPR['Entity'] == 'Tunisia']
FemaleLFPR_Tunisia.reset_index(drop = True)
FemaleLFPR_Tunisia = FemaleLFPR_Tunisia[['Entity', 'Code', 'Year', 'Labor force participation rate, female (% of female population ages 15+) (modeled ILO estimate)']]
FemaleLFPR_Tunisia
FemaleLFPR_Tunisia.describe()
# Difference between Tunisia and Niger mean: 67.788 - 23.823 = 43.965%
# The LFPR of females in Niger is almost three times as high as the one in Tunisia (67.788 / 23.823 = 2.845)<jupyter_output><empty_output>
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