How to plot a count bar chart with a Pandas DF, grouping by one categorical column and colouring by another
This is my answer:
def plot_bargraph_with_groupings(df, groupby, colourby, title, xlabel, ylabel):
"""
Plots a dataframe showing the frequency of datapoints grouped by one column and coloured by another.
df : dataframe
groupby: the column to groupby
colourby: the column to color by
title: the graph title
xlabel: the x label,
ylabel: the y label
"""
import matplotlib.patches as mpatches
# Makes a mapping from the unique colourby column items to a random color.
ind_col_map = {x:y for x, y in zip(df[colourby].unique(),
[plt.cm.Paired(np.arange(len(df[colourby].unique())))][0])}
# Find when the indicies of the soon to be bar graphs colors.
unique_comb = df[[groupby, colourby]].drop_duplicates()
name_ind_map = {x:y for x, y in zip(unique_comb[groupby], unique_comb[colourby])}
c = df[groupby].value_counts().index.map(lambda x: ind_col_map[name_ind_map[x]])
# Makes the bargraph.
ax = df[groupby].value_counts().plot(kind='bar',
figsize=FIG_SIZE,
title=title,
color=[c.values])
# Makes a legend using the ind_col_map
legend_list = []
for key in ind_col_map.keys():
legend_list.append(mpatches.Patch(color=ind_col_map[key], label=key))
# display the graph.
plt.legend(handles=legend_list)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
It might be a little bit too complicated but this does the work. I first defined the mappings from name to industry and from industry to color (it seems like there are only two industries but you can adjust the dictionary to your case):
ind_col_map = {
"industry1": "red",
"industry2": "blue"
}
unique_comb = df[["Name","industry"]].drop_duplicates()
name_ind_map = {x:y for x, y in zip(unique_comb["Name"],unique_comb["industry"])}
Then the color can be generated by using the above two mappings:
c = df['Name'].value_counts().index.map(lambda x: ind_col_map[name_ind_map[x]])
Finally, you only need to simply add color
to your plotting function:
ax = df['Name'].value_counts().plot(kind='bar',
figsize=(14,8),
title="Number for each Owner Name", color=c)
ax.set_xlabel("Owner Names")
ax.set_ylabel("Frequency")
plt.show()
Let's use a some dataframe reshaping and matplotlib:
ax = df.groupby(['industry','Name'])['Name'].count().unstack(0).plot.bar(title="Number for each Owner Name", figsize=(14,8))
_ = ax.set_xlabel('Owner')
_ = ax.set_ylabel('Frequency')
Output: