I was doing this project and just noticed that unlike all projects there is no video walkthrough available for this one. So thought it might help someone who is having trouble completing it. 
Here’s my code-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import codecademylib3_seaborn
import glob
files = glob.glob(‘states*.csv’)
data_list = [pd.read_csv(file) for file in files]
us_census = pd.concat(data_list)
print(us_census.columns)
us_census[‘Income’] = us_census.Income.str[1:]
us_census[‘Income’] = pd.to_numeric(us_census.Income)
gender_split = us_census.GenderPop.str.split(’_’)
us_census[‘Men’] = gender_split.str.get(0)
us_census[‘Women’] = gender_split.str.get(1)
replace ‘M’ & ‘F’
us_census[‘Men’] = us_census.Men.str[:-1]
us_census[‘Women’] = us_census.Women.str[:-1]
to number conversion
us_census[‘Men’] = pd.to_numeric(us_census.Men)
us_census[‘Women’] = pd.to_numeric(us_census.Women)
us_census = us_census.fillna(value={
‘Women’: us_census.TotalPop - us_census.Men
})
duplicates = us_census.duplicated(subset=[‘State’])
print(duplicates.value_counts())
us_census = us_census.drop_duplicates()
plt.scatter(us_census[‘Women’], us_census[‘Income’], color=[‘red’,‘green’])
plt.xlabel(‘Women’)
plt.ylabel(‘Income’)
plt.show()
plt.cla()
us_census[‘Hispanic’] = us_census.Hispanic.str[:-1]
us_census[‘Hispanic’] = pd.to_numeric(us_census.Hispanic)
us_census[‘White’] = us_census.White.str[:-1]
us_census[‘White’] = pd.to_numeric(us_census.White)
us_census[‘Black’] = us_census.Black.str[:-1]
us_census[‘Black’] = pd.to_numeric(us_census.Black)
us_census[‘Native’] = us_census.Native.str[:-1]
us_census[‘Native’] = pd.to_numeric(us_census.Native)
us_census[‘Asian’] = us_census.Asian.str[:-1]
us_census[‘Asian’] = pd.to_numeric(us_census.Asian)
us_census[‘Pacific’] = us_census.Pacific.str[:-1]
us_census[‘Pacific’] = pd.to_numeric(us_census.Pacific)
us_census = us_census.fillna(value={
‘Hispanic’: us_census.Hispanic.mean(),
‘White’: us_census.White.mean(),
‘Black’: us_census.Black.mean(),
‘Native’: us_census.Native.mean(),
‘Asian’: us_census.Asian.mean(),
‘Pacific’: us_census.Pacific.mean(),
})
plt.hist(us_census[‘Hispanic’])
plt.title(‘Hispanic’)
plt.show()
plt.cla()
plt.hist(us_census[‘White’])
plt.title(‘White’)
plt.show()
plt.cla()
plt.hist(us_census[‘Black’])
plt.title(‘Black’)
plt.show()
plt.cla()
plt.hist(us_census[‘Native’])
plt.title(‘Native’)
plt.show()
plt.cla()
plt.hist(us_census[‘Pacific’])
plt.title(‘Pacific’)
plt.show()
plt.cla()
plt.hist(us_census[‘Asian’])
plt.title(‘Asian’)
plt.show()
print(us_census.head())
print(us_census.dtypes)
