Python

Python is an interpreted high-level general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant indentation.

While I get the benefits of IDEs, I tend to find them nausating with all the windows and buttons on the screen

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While I get the benefits of IDEs, I tend to find them nausating with all the windows and buttons on the screen. I use the geany text editor for many of my coding projects — it is an IDE / text editor, with fewer buttons and a much faster interface. Other times I use Jupyter, especially when I am developing Python or R code.

I get the benefits of having object explorers and integrated debuggers — but you can get a variable inspector plug-in for Jupyter, and can often break up code into short enough segments for easy debugging in Jupyter.

Extracting from the ACS

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Here is some code I wrote for creating Census graphics from the American Community Survey data. It could be generalized, but it works for now.

import pandas as pd

path = '/media/hd2/census/acs-summary-file/'

# much faster to load the one ny state sheet as csv
geo = pd.read_csv(path+'5_year_Mini_Geo_NY.csv', index_col='Logical Record Number')

# useful fields for querying
geo['summary_level']=geo['Geography ID'].str[0:3]
geo['state_id']=geo['Geography ID'].str[7:9]
geo['county_id']=geo['Geography ID'].str[9:12]
geo['county_sub']=geo['Geography ID'].str[12:18]
geo['geoid']=geo['Geography ID'].str[7:18]

# load headers
header = pd.read_excel(path+'ACS_5yr_Seq_Table_Number_Lookup.xlsx')

# create a column with census variable headers
header['COL_NAME'] = header['Table ID'] + '_' + header['Line Number'].apply(lambda a: "{0:.0f}".format(a).zfill(3))

# segment id, along with ACS year and state
segId = 33
year = 2019
state = 'ny'
geolevel=''

# create a list of headers for segment file
segHead = ['FILEID','FILETYPE','STUSAB','CHARITER','SEQUENCE','LOGRECNO'] \
    + header.query('`Sequence Number` == '+str(segId)).dropna(subset=['Line Number'])['COL_NAME'].to_list()
    
seg = pd.read_csv(path+'e'+str(year)+'5'+state+(str(segId).zfill(4))+'000.txt'+geolevel+'.zip',header=None, names=segHead, index_col=5)

seg = geo.join(seg)

# make the pretty graphs

import matplotlib.pyplot as plt
from datetime import date

plt.rcParams['font.family'] = 'Nimbus Sans' #'Overpass Mono'
plt.rcParams['font.size'] = 14

plt.rcParams['figure.figsize'] = [10, 10]

colors = plt.get_cmap('plasma', 11).colors

counties_fips=seg['county_id'].unique()

for county_fips in counties_fips:
    if not county_fips: continue
    print('County FIPS: '+county_fips)
    
    towns=seg[((seg['county_id'] == county_fips) & (seg['summary_level'] == '060'))].sort_values(by='Geography Name', ascending=False)
    
    fig, ax = plt.subplots()
    
    # clean up these variables
    for var in ['B09005_001','B09005_002','B09005_003','B09005_004','B09005_005']:
        towns[var]=pd.to_numeric(towns[var],errors='coerce')
        towns=towns.dropna(axis=0, subset=[var])

    
    # iterate through each line so we can see if big enough to add label
    for i, key in enumerate(towns.index):  
    
		# suppress towns with few lines
        town = towns.loc[key]
        if town['B09005_001'] < 5: continue
        

        # plot bars
        left = 0
        
        # choose colors that reinforce gender stereotypes
        for var, label, color in zip(['B09005_002','B09005_003','B09005_004','B09005_005'], 
                             ['Married','Cohabitating','Men','Women'],
                             ['SeaGreen','IndianRed','DarkBlue','Orchid']):
            
            per = (town[var]/town['B09005_001'])*100
            
            if i != 0:
                p1=ax.barh(town['Geography Name'].split(',')[0], per, 
                          color=color, left=left)            
            else:
                p1=ax.barh(town['Geography Name'].split(',')[0], per, 
                          color=color, label=label, left=left)

			# label bars above 10%
            if per > 10:
                ax.bar_label(p1, label_type='center',weight=1000, size=14, backgroundcolor='#ffffff66', fmt='%.1f%%')

            left += per

    county = towns.iloc[0]['Geography Name'].split(', ')[1]
    
    plt.figtext(.5, 1.08, county+' Household Type for Families with Children', horizontalalignment='center', 
                fontsize=22, fontweight=1000) 
    
    plt.figtext(.5, 1.03, 'While the traditional married couple is the most common way '+\
                'of raising children across the state,\n single mothers are more common especially in the cities. Single men households remain relatively rate.', 
                horizontalalignment='center', 
                fontsize=12, fontstyle='italic', fontweight=100) 
    
    plt.figtext(1, -0.01, '2019 5-yr American Community Survey, Table No. B09005, Seq. 33', wrap=True, horizontalalignment='right', fontstyle='italic', fontsize=14, color='#00000088')
    plt.figtext(0, -0.01, 'Andy Arthur, '+date.today().strftime('%m/%-d/%y'), wrap=True, horizontalalignment='left', fontstyle='italic', fontsize=14, color='#00000088')

    plt.margins(0,0.02)
    ax.xaxis.set_ticks_position('bottom')
    ax.yaxis.set_ticks_position('none')
    #ax.xaxis.grid(False)
    ax.spines.top.set_visible(False)
    ax.spines.bottom.set_visible(False)
    ax.spines.left.set_visible(False)
    ax.spines.right.set_visible(False)
    
    fig.patch.set_facecolor('White')

    plt.tight_layout()
    
    #ax.xaxis.set_ticks([0, 50, 100])
    
    plt.legend(bbox_to_anchor=(0., 1, 1., .102), loc='lower center',
          ncol=4, mode="expand", borderaxespad=0., edgecolor='white')
    
    #plt.show()
    #break
    plt.savefig('/tmp/household/'+county+'_child_household.png',dpi=150,bbox_inches='tight')