Multiply two columns of Census data and groupby











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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="")









share|improve this question
















bumped to the homepage by Community 13 hours ago


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  • Hmm... are you going to keep it here? it appears you posted working code...
    – Sᴀᴍ Onᴇᴌᴀ
    May 16 at 16:45










  • Yeah I think this question belongs here, what do you think? My working code wasn't so obvious the first time. Sorry for the confusion
    – Patrick Stetz
    May 16 at 16:48






  • 1




    @Graipher In its current state, the code seems to work and he's asking for a better approach. This seems sufficiently on-topic to me.
    – scnerd
    May 16 at 17:07






  • 1




    Hi Sam, the variable census is the data I'm looking at and can be found here. I'll edit my question to include this too
    – Patrick Stetz
    May 16 at 17:22










  • @scnerd I agree, the current question is on-topic.
    – Graipher
    May 16 at 18:29















up vote
0
down vote

favorite












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="")









share|improve this question
















bumped to the homepage by Community 13 hours ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.















  • Hmm... are you going to keep it here? it appears you posted working code...
    – Sᴀᴍ Onᴇᴌᴀ
    May 16 at 16:45










  • Yeah I think this question belongs here, what do you think? My working code wasn't so obvious the first time. Sorry for the confusion
    – Patrick Stetz
    May 16 at 16:48






  • 1




    @Graipher In its current state, the code seems to work and he's asking for a better approach. This seems sufficiently on-topic to me.
    – scnerd
    May 16 at 17:07






  • 1




    Hi Sam, the variable census is the data I'm looking at and can be found here. I'll edit my question to include this too
    – Patrick Stetz
    May 16 at 17:22










  • @scnerd I agree, the current question is on-topic.
    – Graipher
    May 16 at 18:29













up vote
0
down vote

favorite









up vote
0
down vote

favorite











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="")









share|improve this question















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="")






python python-3.x csv pandas






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share|improve this question








edited May 16 at 23:02

























asked May 16 at 4:51









Patrick Stetz

245




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bumped to the homepage by Community 13 hours ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.







bumped to the homepage by Community 13 hours ago


This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.














  • Hmm... are you going to keep it here? it appears you posted working code...
    – Sᴀᴍ Onᴇᴌᴀ
    May 16 at 16:45










  • Yeah I think this question belongs here, what do you think? My working code wasn't so obvious the first time. Sorry for the confusion
    – Patrick Stetz
    May 16 at 16:48






  • 1




    @Graipher In its current state, the code seems to work and he's asking for a better approach. This seems sufficiently on-topic to me.
    – scnerd
    May 16 at 17:07






  • 1




    Hi Sam, the variable census is the data I'm looking at and can be found here. I'll edit my question to include this too
    – Patrick Stetz
    May 16 at 17:22










  • @scnerd I agree, the current question is on-topic.
    – Graipher
    May 16 at 18:29


















  • Hmm... are you going to keep it here? it appears you posted working code...
    – Sᴀᴍ Onᴇᴌᴀ
    May 16 at 16:45










  • Yeah I think this question belongs here, what do you think? My working code wasn't so obvious the first time. Sorry for the confusion
    – Patrick Stetz
    May 16 at 16:48






  • 1




    @Graipher In its current state, the code seems to work and he's asking for a better approach. This seems sufficiently on-topic to me.
    – scnerd
    May 16 at 17:07






  • 1




    Hi Sam, the variable census is the data I'm looking at and can be found here. I'll edit my question to include this too
    – Patrick Stetz
    May 16 at 17:22










  • @scnerd I agree, the current question is on-topic.
    – Graipher
    May 16 at 18:29
















Hmm... are you going to keep it here? it appears you posted working code...
– Sᴀᴍ Onᴇᴌᴀ
May 16 at 16:45




Hmm... are you going to keep it here? it appears you posted working code...
– Sᴀᴍ Onᴇᴌᴀ
May 16 at 16:45












Yeah I think this question belongs here, what do you think? My working code wasn't so obvious the first time. Sorry for the confusion
– Patrick Stetz
May 16 at 16:48




Yeah I think this question belongs here, what do you think? My working code wasn't so obvious the first time. Sorry for the confusion
– Patrick Stetz
May 16 at 16:48




1




1




@Graipher In its current state, the code seems to work and he's asking for a better approach. This seems sufficiently on-topic to me.
– scnerd
May 16 at 17:07




@Graipher In its current state, the code seems to work and he's asking for a better approach. This seems sufficiently on-topic to me.
– scnerd
May 16 at 17:07




1




1




Hi Sam, the variable census is the data I'm looking at and can be found here. I'll edit my question to include this too
– Patrick Stetz
May 16 at 17:22




Hi Sam, the variable census is the data I'm looking at and can be found here. I'll edit my question to include this too
– Patrick Stetz
May 16 at 17:22












@scnerd I agree, the current question is on-topic.
– Graipher
May 16 at 18:29




@scnerd I agree, the current question is on-topic.
– Graipher
May 16 at 18:29










1 Answer
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oldest

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up vote
0
down vote













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)





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    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)





    share|improve this answer

























      up vote
      0
      down vote













      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)





      share|improve this answer























        up vote
        0
        down vote










        up vote
        0
        down vote









        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)





        share|improve this answer












        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)






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered May 17 at 8:56









        JahKnows

        1011




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