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plotly_year_functions.py
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plotly_year_functions.py
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#################################################################################
#################################################################################
#################################################################################
#################################################################################
#################################################################################
#################################################################################
def basic_hist(df, year, col, title=None,
template='seaborn', save=False, fig_name=None):
"""Takes a DataFrame with a year column, filters the DataFrame to the
provided election year, and returns a color-coded Plotly histogram
for the provided column.
Args:
df (DataFrame): A Pandas DataFrame
year (int): Election year (2012, 2016, or 2020 only)
col (str): Name of the df column for which to plot histogram
title (str, optional): Title for the resulting plot. If none is provided,
defaults to 'Distribution of labels[{col}] in {year} General Election'.
template (str, optional): Plotly style template. Defaults to 'seaborn'.
save (bool, default=False): Whether to save the returned figure. Defaults to False.
fig_name (str, optional): What to name the file if the image is being saved.
Defaults to None.
Returns:
Figure: Returns Plotly histogram of provided column for the specified year.
"""
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
fig_filepath = 'Figures/'
title_font_dict = {
'family':'Arial Black',
'size':24
}
ax_title_font_dict = {
'family':'Arial Black',
'size':18
}
ax_tick_font_dict = {
'family':'Arial Black',
'size':15
}
cat_orders = {}
labels = {}
if col == 'gen_grp':
color_map = {
'Greatest-Silent': 'orchid',
'Boomer': 'dodgerblue',
'GenX': 'mediumspringgreen',
'Millennial': 'gold',
'GenZ': 'coral'
}
cat_orders.update({'gen_grp': ['GenZ', 'Millennial', 'GenX',
'Boomer', 'Greatest-Silent']})
labels.update({'gen_grp': 'Generation'})
if col == 'party_grp':
color_map = {
'Dem': 'blue',
'Rep': 'red',
'Other': 'gold'
}
cat_orders.update({'party_grp': ['Dem', 'Rep', 'Other']})
labels.update({'party_grp': 'Party'})
if col == 'vote_method_4':
color_map = {
'Early': 'navy',
'No Vote': 'goldenrod',
'Election Day': 'teal',
'Other': 'aqua'
}
cat_orders.update({'vote_method_4': ['Early', 'No Vote',
'Election Day', 'Other']})
labels.update({'vote_method_4': 'Voting Method'})
if col == 'vote_method_5':
color_map = {
'Early': 'navy',
'No Vote': 'goldenrod',
'Election Day': 'teal',
'Mail': 'blue',
'Other': 'aqua'
}
cat_orders.update({'vote_method_5': ['Early', 'No Vote',
'Election Day', 'Mail',
'Other']})
labels.update({'vote_method_5': 'Voting Method'})
if col == 'vote_bin':
color_map = {
'Y': 'blue',
'N': 'goldenrod'
}
cat_orders.update({'vote_bin': ['Y', 'N']})
labels.update({'vote_bin': 'Voted (Y/N)'})
if col == 'pri_vote_bin':
color_map = {
'Y': 'blue',
'N': 'goldenrod'
}
cat_orders.update({'pri_vote_bin': ['Y', 'N']})
labels.update({'pri_vote_bin': 'Voted in Primary'})
if col == 'race_grp':
color_map = {
'White': 'forestgreen',
'Black': 'firebrick',
'Undesig.': 'mediumslateblue',
'Other': 'fuchsia'
}
cat_orders.update({'race_grp': ['White',
'Black',
'Undesig.',
'Other']})
labels.update({'race_grp': 'Race'})
if col == 'gender_code':
color_map = {
'F': 'deeppink',
'M': 'deepskyblue',
'U': 'lawngreen'
}
cat_orders.update({'gender_code': ['F', 'M', 'U']})
labels.update({'gender_code': 'Gender'})
if col == 'birth_reg_other':
color_map = {
'South': '#AB63FA',
'Missing': '#FFA15A',
'Northeast': '#19D3F3',
'Midwest': '#FF6692',
'Other': '#B6E880',
'West': '#FF97FF'
}
cat_orders.update({'birth_reg_other': ['South',
'Missing',
'Northeast',
'Midwest',
'Other',
'West']})
labels.update({'birth_reg_other': 'Birth Region'})
if col == 'drivers_lic':
color_map = {
'License': 'green',
'No License': 'crimson'
}
cat_orders.update(
{
'drivers_lic': [
'License',
'No License'
]
}
)
labels.update({'drivers_lic': 'Drivers License'})
if col == 'city_grp':
color_map = {
'Monroe': '#FD3216',
'Waxhaw': '#00FE35',
'Indian Trail': '#6A76FC',
'Matthews': '#0DF9FF',
'Other': '#F6F926'
}
cat_orders.update({'city_grp': ['Monroe',
'Waxhaw',
'Indian Trail',
'Matthews',
'Other']})
labels.update({'city_grp': 'City'})
filtered_df = df.loc[df['year']==int(year)]
if col == 'birth_age_adj':
labels.update({'birth_age_adj': 'Age'})
fig = px.histogram(filtered_df, x=col,
title='Distribution of {} <br> in {} General Election'.format(
labels[col], str(year)
),
color_discrete_sequence=['dodgerblue'],
category_orders=cat_orders,
labels=labels,
template=template,
nbins=50
)
else:
fig = px.histogram(filtered_df, x=col, color=col,
color_discrete_map=color_map,
title='Distribution of {} <br> in {} General Election'.format(
labels[col], str(year)
),
category_orders=cat_orders,
labels=labels,
template=template
)
fig.update_layout(
title_font=title_font_dict,
showlegend=False
)
fig.update_yaxes(
title='Number of Registered Voters',
title_font=ax_title_font_dict,
tickfont=ax_tick_font_dict
)
fig.update_xaxes(
title_font=ax_title_font_dict,
tickfont=ax_tick_font_dict
)
if save:
fig.write_html(fig_filepath+fig_name+'.html')
return fig
#################################################################################
#################################################################################
#################################################################################
#################################################################################
#################################################################################
#################################################################################
def grp_hist(df, year, group_col_1, group_col_2, title=None,
barmode='group', histnorm=None,
template='seaborn', save=False, fig_name=None):
"""Takes a DataFrame with a year column, groups the df by the first
column specified, then color-codes by the second provided column
to create a Plotly histogram for the specified election year.
Args:
df (DataFrame): A Pandas DataFrame
year (int): Election year (2012, 2016, or 2020 only)
group_col_1 (str): Name of the df column by which to group
group_col_2 (str): Name of the df column by which to color-code the histograms
title (str, optional): Title for the resulting plot. If none is provided,
defaults to 'labels[{group_col_1}] by labels[{group_col_2}] in {year} General Election'.
barmode (str, optional): Plotly barmode parameter. Defaults to 'group'.
histnorm (str, optional): Plotly histnorm parameter, but only takes None or 'percent'.
Defaults to None.
template (str, optional): Plotly style template. Defaults to 'seaborn'.
save (bool, default=False): Whether to save the returned figure. Defaults to False.
fig_name (str, optional): What to name the file if the image is being saved.
Defaults to None.
Returns:
Figure: Plotly histogram grouped by group_col_1 and color-coded
according to group_col_2 for the specified election year.
"""
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
fig_filepath = 'Figures/'
title_dict = {
'font' : {
'family':'Arial Black',
'size':24
}
}
leg_dict = {
'font' : {
'family':'Arial Black',
'size':13
},
'title': ''
}
ax_title_font_dict = {
'family':'Arial Black',
'size':18
}
ax_tick_font_dict = {
'family':'Arial Black',
'size':15
}
cat_orders = {}
labels = {}
if group_col_2 == 'gen_grp':
color_map = {
'Greatest-Silent': 'orchid',
'Boomer': 'dodgerblue',
'GenX': 'mediumspringgreen',
'Millennial': 'gold',
'GenZ': 'coral'
}
if (group_col_1 == 'gen_grp') | (group_col_2 == 'gen_grp'):
cat_orders.update({'gen_grp': ['GenZ', 'Millennial', 'GenX',
'Boomer', 'Greatest-Silent']})
labels.update({'gen_grp': 'Generation'})
if group_col_2 == 'party_grp':
color_map = {
'Dem': 'blue',
'Rep': 'red',
'Other': 'gold'
}
if (group_col_1 == 'party_grp') | (group_col_2 == 'party_grp'):
cat_orders.update({'party_grp': ['Dem', 'Rep', 'Other']})
labels.update({'party_grp': 'Party'})
if group_col_2 == 'vote_method_4':
color_map = {
'Early': 'navy',
'No Vote': 'goldenrod',
'Election Day': 'teal',
'Other': 'aqua'
}
if (group_col_1 == 'vote_method_4') | (group_col_2 == 'vote_method_4'):
cat_orders.update({'vote_method_4': ['Early', 'No Vote',
'Election Day', 'Other']})
labels.update({'vote_method_4': 'Voting Method'})
if group_col_2 == 'vote_method_5':
color_map = {
'Early': 'navy',
'No Vote': 'goldenrod',
'Election Day': 'teal',
'Mail': 'blue',
'Other': 'aqua'
}
if (group_col_1 == 'vote_method_5') | (group_col_2 == 'vote_method_5'):
cat_orders.update({'vote_method_5': ['Early', 'No Vote',
'Election Day', 'Mail',
'Other']})
labels.update({'vote_method_5': 'Voting Method'})
if group_col_2 == 'vote_bin':
color_map = {
'Y': 'blue',
'N': 'goldenrod'
}
if (group_col_1 == 'vote_bin') | (group_col_2 == 'vote_bin'):
cat_orders.update({'vote_bin': ['Y', 'N']})
labels.update({'vote_bin': 'Voted (Y/N)'})
if group_col_2 == 'pri_vote_bin':
color_map = {
'Y': 'blue',
'N': 'goldenrod'
}
if (group_col_1 == 'pri_vote_bin') | (group_col_2 == 'pri_vote_bin'):
cat_orders.update({'pri_vote_bin': ['Y', 'N']})
labels.update({'pri_vote_bin': 'Voted in Primary'})
if group_col_2 == 'race_grp':
color_map = {
'White': 'forestgreen',
'Black': 'firebrick',
'Undesig.': 'mediumslateblue',
'Other': 'fuchsia'
}
if (group_col_1 == 'race_grp') | (group_col_2 == 'race_grp'):
cat_orders.update({'race_grp': ['White',
'Black',
'Undesig.',
'Other']})
labels.update({'race_grp': 'Race'})
if group_col_2 == 'gender_code':
color_map = {
'F': 'deeppink',
'M': 'deepskyblue',
'U': 'lawngreen'
}
if (group_col_1 == 'gender_code') | (group_col_2 == 'gender_code'):
cat_orders.update({'gender_code': ['F', 'M', 'U']})
labels.update({'gender_code': 'Gender'})
if group_col_2 == 'birth_reg_other':
color_map = {
'South': '#AB63FA',
'Missing': '#FFA15A',
'Northeast': '#19D3F3',
'Midwest': '#FF6692',
'Other': '#B6E880',
'West': '#FF97FF'
}
if (group_col_1 == 'birth_reg_other') | (group_col_2 == 'birth_reg_other'):
cat_orders.update({'birth_reg_other': ['South',
'Missing',
'Northeast',
'Midwest',
'Other',
'West']})
labels.update({'birth_reg_other': 'Birth Region'})
if group_col_2 == 'drivers_lic':
color_map = {
'License': 'green',
'No License': 'crimson'
}
if (group_col_1 == 'drivers_lic') | (group_col_2 == 'drivers_lic'):
cat_orders.update({'drivers_lic': ['License', 'No License']})
labels.update({'drivers_lic': 'Drivers License'})
if group_col_2 == 'city_grp':
color_map = {
'Monroe': '#FD3216',
'Waxhaw': '#00FE35',
'Indian Trail': '#6A76FC',
'Matthews': '#0DF9FF',
'Other': '#F6F926'
}
if (group_col_1 == 'city_grp') | (group_col_2 == 'city_grp'):
cat_orders.update({'city_grp': ['Monroe',
'Waxhaw',
'Indian Trail',
'Matthews',
'Other']})
labels.update({'city_grp': 'City'})
filtered_df = df.loc[df['year']==int(year)]
fig = px.histogram(filtered_df, x=group_col_1, color=group_col_2,
color_discrete_map=color_map, barmode=barmode,
title='{} by {} <br> in {} General Election'.format(
labels[group_col_1],
labels[group_col_2],
str(year)
),
category_orders=cat_orders,
labels=labels,
template=template,
histnorm=histnorm
)
if histnorm=='percent':
fig.update_yaxes(title='Percent of Registered Voters')
if histnorm==None:
fig.update_yaxes(title='Number of Registered Voters')
fig.update_layout(
title=title_dict,
legend = leg_dict
)
fig.update_yaxes(
title_font=ax_title_font_dict,
tickfont=ax_tick_font_dict
)
fig.update_xaxes(
title_font=ax_title_font_dict,
tickfont=ax_tick_font_dict
)
if save:
fig.write_html(fig_filepath+fig_name+'.html')
return fig
#################################################################################
#################################################################################
#################################################################################
#################################################################################
#################################################################################
#################################################################################
def stack_grp_hist(df, year, group_col_1, group_col_2, title=None,
percent=None, template='seaborn',
save=False, fig_name=None):
"""Takes a DataFrame with a year column, groups the df by the first
column specified, then color-codes by the second provided column
to create a stacked Plotly bar chart for the specified election year.
Args:
df (DataFrame): A Pandas DataFrame
year (int): Election year (2012, 2016, or 2020 only)
group_col_1 (str): Name of the df column by which to group
group_col_2 (str): Name of the df column by which to color-code the histograms
title (str, optional): Title for the resulting plot. If none is provided,
defaults to 'labels[{group_col_1}] by labels[{group_col_2}] in {year} General Election'.
barmode (str, optional): Plotly barmode parameter. Defaults to 'group'.
histnorm (str, optional): Plotly histnorm parameter, but only takes None or 'percent'.
Defaults to None.
percent (str, optional): If 'percent', plots bars as percentages.
If None, plots as raw counts. Defaults to None.
template (str, optional): Plotly style template. Defaults to 'seaborn'.
save (bool, optional): Whether to save the returned figure. Defaults to False.
fig_name (str, optional): What to name the file if the image is being saved.
Defaults to None.
Returns:
Figure: Plotly stacked bar chart grouped by group_col_1 and color-coded
according to group_col_2 for the specified election year.
"""
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
fig_filepath = 'Figures/'
title_dict = {
'font' : {
'family':'Arial Black',
'size':24
}
}
leg_dict = {
'font' : {
'family':'Arial Black',
'size':13
},
'title': ''
}
ax_title_font_dict = {
'family':'Arial Black',
'size':18
}
ax_tick_font_dict = {
'family':'Arial Black',
'size':15
}
cat_orders = {}
labels = {}
if group_col_2 == 'gen_grp':
color_map = {
'Greatest-Silent': 'orchid',
'Boomer': 'dodgerblue',
'GenX': 'mediumspringgreen',
'Millennial': 'gold',
'GenZ': 'coral'
}
if (group_col_1 == 'gen_grp') | (group_col_2 == 'gen_grp'):
cat_orders.update({'gen_grp': ['GenZ', 'Millennial', 'GenX',
'Boomer', 'Greatest-Silent']})
labels.update({'gen_grp': 'Generation'})
if group_col_2 == 'party_grp':
color_map = {
'Dem': 'blue',
'Rep': 'red',
'Other': 'gold'
}
if (group_col_1 == 'party_grp') | (group_col_2 == 'party_grp'):
cat_orders.update({'party_grp': ['Dem', 'Rep', 'Other']})
labels.update({'party_grp': 'Party'})
if group_col_2 == 'vote_method_4':
color_map = {
'Early': 'navy',
'No Vote': 'goldenrod',
'Election Day': 'teal',
'Other': 'aqua'
}
if (group_col_1 == 'vote_method_4') | (group_col_2 == 'vote_method_4'):
cat_orders.update({'vote_method_4': ['Early', 'No Vote',
'Election Day', 'Other']})
labels.update({'vote_method_4': 'Voting Method'})
if group_col_2 == 'vote_method_5':
color_map = {
'Early': 'navy',
'No Vote': 'goldenrod',
'Election Day': 'teal',
'Mail': 'blue',
'Other': 'aqua'
}
if (group_col_1 == 'vote_method_5') | (group_col_2 == 'vote_method_5'):
cat_orders.update({'vote_method_5': ['Early', 'No Vote',
'Election Day', 'Mail',
'Other']})
labels.update({'vote_method_5': 'Voting Method'})
if group_col_2 == 'vote_bin':
color_map = {
'Y': 'blue',
'N': 'goldenrod'
}
if (group_col_1 == 'vote_bin') | (group_col_2 == 'vote_bin'):
cat_orders.update({'vote_bin': ['Y', 'N']})
labels.update({'vote_bin': 'Voted (Y/N)'})
if group_col_2 == 'pri_vote_bin':
color_map = {
'Y': 'blue',
'N': 'goldenrod'
}
if (group_col_1 == 'pri_vote_bin') | (group_col_2 == 'pri_vote_bin'):
cat_orders.update({'pri_vote_bin': ['Y', 'N']})
labels.update({'pri_vote_bin': 'Voted in Primary'})
if group_col_2 == 'race_grp':
color_map = {
'White': 'forestgreen',
'Black': 'firebrick',
'Undesig.': 'mediumslateblue',
'Other': 'fuchsia'
}
if (group_col_1 == 'race_grp') | (group_col_2 == 'race_grp'):
cat_orders.update({'race_grp': ['White',
'Black',
'Undesig.',
'Other']})
labels.update({'race_grp': 'Race'})
if group_col_2 == 'gender_code':
color_map = {
'F': 'deeppink',
'M': 'deepskyblue',
'U': 'lawngreen'
}
if (group_col_1 == 'gender_code') | (group_col_2 == 'gender_code'):
cat_orders.update({'gender_code': ['F', 'M', 'U']})
labels.update({'gender_code': 'Gender'})
if group_col_2 == 'birth_reg_other':
color_map = {
'South': '#AB63FA',
'Missing': '#FFA15A',
'Northeast': '#19D3F3',
'Midwest': '#FF6692',
'Other': '#B6E880',
'West': '#FF97FF'
}
if (group_col_1 == 'birth_reg_other') | (group_col_2 == 'birth_reg_other'):
cat_orders.update({'birth_reg_other': ['South',
'Missing',
'Northeast',
'Midwest',
'Other',
'West']})
labels.update({'birth_reg_other': 'Birth Region'})
if group_col_2 == 'drivers_lic':
color_map = {
'License': 'green',
'No License': 'crimson'
}
if (group_col_1 == 'drivers_lic') | (group_col_2 == 'drivers_lic'):
cat_orders.update({'drivers_lic': ['License', 'No License']})
labels.update({'drivers_lic': 'Drivers License'})
if group_col_2 == 'city_grp':
color_map = {
'Monroe': '#FD3216',
'Waxhaw': '#00FE35',
'Indian Trail': '#6A76FC',
'Matthews': '#0DF9FF',
'Other': '#F6F926'
}
if (group_col_1 == 'city_grp') | (group_col_2 == 'city_grp'):
cat_orders.update({'city_grp': ['Monroe',
'Waxhaw',
'Indian Trail',
'Matthews',
'Other']})
labels.update({'city_grp': 'City'})
filtered_df = df.loc[df['year']==int(year)].copy()
df_slice = filtered_df[[group_col_1, group_col_2, 'year']]
grpby_slice = df_slice.groupby([group_col_1, group_col_2]).count()
grpby_slice.reset_index(inplace=True)
grpby_slice.rename(columns={'year':'Count'}, inplace=True)
total_count_slice = df_slice.drop(
columns=['year']
).groupby([group_col_1]).count()
total_count_slice.reset_index(inplace=True)
total_count_slice.rename(columns={group_col_2:'Total'}, inplace=True)
merge_slice = grpby_slice.merge(total_count_slice, on=group_col_1)
merge_slice['Percent'] = round(
(merge_slice['Count'] / merge_slice['Total'])*100, 2
)
if percent:
fig = px.bar(merge_slice, x=group_col_1, y='Percent',
color=group_col_2, color_discrete_map=color_map,
title='{} by {} <br> in {} General Election'.format(
labels[group_col_1],
labels[group_col_2],
str(year)
),
category_orders=cat_orders,
labels=labels,
template=template
)
fig.update_yaxes(title='Percent of Registered Voters')
else:
fig = px.bar(merge_slice, x=group_col_1, y='Count',
color=group_col_2, color_discrete_map=color_map,
title='{} by {} <br> in {} General Election'.format(
labels[group_col_1],
labels[group_col_2],
str(year)
),
category_orders=cat_orders,
labels=labels,
template=template
)
fig.update_yaxes(title='Number of Registered Voters')
fig.update_layout(
title=title_dict,
legend = leg_dict
)
fig.update_yaxes(
title_font=ax_title_font_dict,
tickfont=ax_tick_font_dict
)
fig.update_xaxes(
title_font=ax_title_font_dict,
tickfont=ax_tick_font_dict
)
if save:
fig.write_html(fig_filepath+fig_name+'.html')
return fig
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def grp_yr_hist(df, group_col_1, title=None, barmode='group',
histnorm=None, template='seaborn', save=False, fig_name=None):
"""Takes a DataFrame with a year column, groups the df by the first
group_col_1, then color-codes by election year to create a Plotly histogram.
Args:
df (DataFrame): A Pandas DataFrame
group_col_1 (str): Name of the df column by which to group
title (str, optional): Title for the resulting plot. If none is provided,
defaults to 'labels[{group_col_1}] by Election Year'.
barmode (str, optional): Plotly barmode parameter. Defaults to 'group'.
histnorm (str, optional): Plotly histnorm parameter, but only takes None or 'percent'.
Defaults to None.
template (str, optional): Plotly style template. Defaults to 'seaborn'.
save (bool, default=False): Whether to save the returned figure. Defaults to False.
fig_name (str, optional): What to name the file if the image is being saved.
Defaults to None.
Returns:
Figure: Plotly histogram grouped by group_col_1 and color-coded
according to group_col_2 for the specified election year.
"""
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
fig_filepath = 'Figures/'
title_dict = {
'font' : {
'family':'Arial Black',
'size':24
},
'y': 0.85
}
leg_dict = {
'font' : {
'family':'Arial Black',
'size':13
},
'title': ''
}
ax_title_font_dict = {
'family':'Arial Black',
'size':18
}
ax_tick_font_dict = {
'family':'Arial Black',
'size':15
}
cat_orders = {'year': [2012,2016,2020]}
labels = {'year': 'Election Year'}
color_map = {
2012: 'darkviolet',
2016: 'limegreen',
2020: 'orangered'
}
if group_col_1 == 'gen_grp':
cat_orders.update({'gen_grp': ['GenZ', 'Millennial', 'GenX',
'Boomer', 'Greatest-Silent']})
labels.update({'gen_grp': 'Generation'})
if group_col_1 == 'party_grp':
cat_orders.update({'party_grp': ['Dem', 'Rep', 'Other']})
labels.update({'party_grp': 'Party'})
if group_col_1 == 'vote_method_4':
cat_orders.update({'vote_method_4': ['Early', 'No Vote',
'Election Day', 'Other']})
labels.update({'vote_method_4': 'Voting Method'})
if group_col_1 == 'vote_method_5':
cat_orders.update({'vote_method_5': ['Early', 'No Vote',
'Election Day', 'Mail',
'Other']})
labels.update({'vote_method_5': 'Voting Method'})
if group_col_1 == 'vote_bin':
cat_orders.update({'vote_bin': ['Y', 'N']})
labels.update({'vote_bin': 'Voted (Y/N)'})
if group_col_1 == 'pri_vote_bin':
cat_orders.update({'pri_vote_bin': ['Y', 'N']})
labels.update({'pri_vote_bin': 'Voted in Primary'})
if group_col_1 == 'race_grp':
cat_orders.update({'race_grp': ['White',
'Black',
'Undesig.',
'Other']})
labels.update({'race_grp': 'Race'})
if group_col_1 == 'gender_code':
cat_orders.update({'gender_code': ['F', 'M', 'U']})
labels.update({'gender_code': 'Gender'})
if group_col_1 == 'birth_reg_other':
cat_orders.update({'birth_reg_other': ['South',
'Missing',
'Northeast',
'Midwest',
'Other',
'West']})
labels.update({'birth_reg_other': 'Birth Region'})
if group_col_1 == 'drivers_lic':
cat_orders.update(
{
'drivers_lic': [
'License',
'No License'
]
}
)
labels.update({'drivers_lic': 'Drivers License'})
if group_col_1 == 'city_grp':
cat_orders.update({'city_grp': ['Monroe',
'Waxhaw',
'Indian Trail',
'Matthews',
'Other']})
labels.update({'city_grp': 'City'})
if group_col_1 == 'birth_age_adj':
labels.update({'birth_age_adj': 'Age'})
fig = px.histogram(df, x=group_col_1, color='year',
color_discrete_map=color_map, barmode=barmode,
title='{} by {}'.format(
labels[group_col_1], labels['year']
),
category_orders=cat_orders,
labels=labels,
template=template,
histnorm=histnorm,
nbins=50
)
else:
fig = px.histogram(df, x=group_col_1, color='year',
color_discrete_map=color_map, barmode=barmode,
title='{} by {}'.format(
labels[group_col_1], labels['year']
),
category_orders=cat_orders,
labels=labels,
template=template,
histnorm=histnorm
)
if histnorm=='percent':
fig.update_yaxes(title='Percent of Registered Voters')
if histnorm==None:
fig.update_yaxes(title='Number of Registered Voters')
fig.update_layout(
title=title_dict,
legend=leg_dict
)
fig.update_yaxes(
title_font=ax_title_font_dict,
tickfont=ax_tick_font_dict
)