#!/usr/bin/env python
# coding: utf-8

# https://api.census.gov/data/2010/dec/sf1/examples.html
# 
# Example of extracting census attribute data (Block population and block black population), e.g. mimicking AFF query and extraction. 

# In[1]:


#Imports
import pandas as pd
import os,requests


# In[2]:


#Get key
pdKey = pd.read_csv('{}/APIkeys.csv'.format(os.environ['localappdata']))
census = pdKey.iloc[0,1]


# List of variables:
# P003001: Total
# P003003: Black or African American alone

# In[3]:


#Census API
theURL = 'https://api.census.gov/data/2010/dec/sf1'
params = {'get':'P003001,P003003,P010001,P010004',
          'for':'block:*',
          'in':'state:37%county:183',
          'key':census
         }
r = requests.get(theURL,params)


# In[9]:


#Convert response to a dataframe
rJSON =r.json()
dfB = pd.DataFrame(rJSON[1:],columns=rJSON[0])
dfB['GEOID10'] = dfB.state+dfB.county+dfB.tract+dfB.block
dfB.head()


# In[11]:


dfB[dfB.GEOID10 == '371830501001020']


# In[84]:


#Export to a table
dfB.iloc[:,[0,1,-1]].to_csv('Durham.csv',index=False)


# Explore variables:
# * Variables: https://api.census.gov/data/2010/dec/sf1/variables.html
# * Groups: https://api.census.gov/data/2010/dec/sf1/groups.html

# In[4]:


#Get list of variables and convert to a dataframe (transposed)
varsJson =requests.get('https://api.census.gov/data/2010/dec/sf1/variables.json').json()
dfVars = pd.DataFrame(varsJson['variables']).T
dfVars.columns


# In[13]:


#Reveal varibles containing the word "Black"
dfVars[dfVars.label.str.contains('Black')]


# In[43]:


df = pd.DataFrame(varsJson['variables']).T
df.head(10)


# In[86]:


df.loc['P010001']#:'P005012']


# In[75]:


df[df.group == 'P10']