Multiply two columns of Census data and groupby I have census data that looks like this

    State   County  TotalPop    Hispanic    White   Black   Native  Asian   Pacific
   Alabama  Autauga     1948    0.9         87.4    7.7     0.3     0.6     0.0
   Alabama  Autauga     2156    0.8         40.4    53.3    0.0     2.3     0.0
   Alabama  Autauga     2968    0.0         74.5    18.6    0.5     1.4     0.3
   ...


Two things to note, (1) there can be multiple rows for a County and (2) the racial data is given in percentages, but sometimes I want the actual size of the population.

Getting the total racial population translates to (in pseudo Pandas):

(census.TotalPop * census.Hispanic / 100).groupby("County").sum()

But, this gives an error: KeyError: 'State'.  As the product of TotalPop and Hispanic is a Pandas Series not the original dataframe.

As suggested by this Stack Overflow question, I can create a new column for each race...

census["HispanicPop"] = census.TotalPop * census.Hispanic / 100

This works, but feels messy, it adds 6 columns unnecessarily as I just need the data for one plot.  Here is the data (I'm using "acs2015_census_tract_data.csv") and here is my implementation:

Working Code

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
%matplotlib inline

census = pd.read_csv("data/acs2015_census_tract_data.csv")

races = ['Hispanic', 'White', 'Black', 'Native', 'Asian', 'Pacific']

# Creating a total population column for each race
# FIXME: this feels inefficient.  Does Pandas have another option?
for race in races:
    census[race + "_pop"] = (census[race] * census.TotalPop) / 100

# current racial population being plotted
race = races[0]

# Sum the populations in each state
race_pops = census.groupby("State")[race + "_pop"].sum().sort_values(ascending=False)

#### Plotting the results for each state

fig, axarr = plt.subplots(2, 2, figsize=(18, 12))
fig.suptitle("{} population in all 52 states".format(race), fontsize=18)

# Splitting the plot into 4 subplots so I can fit all 52 States
data = race_pops.head(13)
sns.barplot(x=data.values, y=data.index, ax=axarr[0][0])

data = race_pops.iloc[13:26]
sns.barplot(x=data.values, y=data.index, ax=axarr[0][1]).set(ylabel="")

data = race_pops.iloc[26:39]
sns.barplot(x=data.values, y=data.index, ax=axarr[1][0])

data = race_pops.tail(13)
_ = sns.barplot(x=data.values, y=data.index, ax=axarr[1][1]).set(ylabel="")
Since you only want to use the total population values for these plots it is not worth adding these columns to your census DataFrame. I would package the plots into a function which creates a temporary DataFrame that is used and then disposed of after the plotting is complete.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
%matplotlib inline

def plot_populations(census, race):
    # Group the data
    race_pops = pd.DataFrame(data={
                                   'State': census['State'], 
                                   'Pop': census[race] * census['TotalPop'] / 100
                                  }
                            ).groupby('State')['Pop'].sum().sort_values(ascending=False)

    # Plot the results
    fig, axarr = plt.subplots(2, 2, figsize=(18, 12))
    fig.suptitle("{} population in all 52 states".format(race), fontsize=18)
    for ix, ax in enumerate(axarr.reshape(-1)):
        data = race_pops.iloc[ix*len(race_pops)//4:(ix+1)*len(race_pops)//4]
        sns.barplot(x=data.values, y=data.index, ax=ax)
        if ix % 2 != 0: ax.set_ylabel('') 


census = pd.read_csv("acs2015_census_tract_data.csv")

races = ['Hispanic', 'White', 'Black', 'Native', 'Asian', 'Pacific']
# current racial population being plotted
race = races[0]

plot_populations(census, race)