I have a dataframe df with columns df.time which outlines the month and time, and df.names so that it looks like this
time names
0 2020-01 Martha
1 2020-01 Jeff
2 2020-01 Geronimo
3 2020-02 Mike
4 2020-02 Michelle
...
I want to get the total number of names per unique value and plot it in a bar chart with months over x and value counts by month on y. I've tried
x=df.time.unique()
y=df.groupby('time').size().sort_values(ascending=True)
For y I'm looking for the total number of names for each unique date in time. But while y does count the total for each time value, it's not in order and I don't know how to link them.
CodePudding user response：
I have managed to find the solution I needed
plot_df=df.groupby(['time']).count().sort_values(['names'],ascending=False).rename(columns={'time':'sum of names'})
plot_df=plot_df.sort_values('time').reset_index()
x=plot_df['time'].astype(str)
y=plot_df['names']
plt.bar(x, height=y, width=0.5)
Thank you to everyone who posted an answer, but this is actually the outcome I was looking for.
CodePudding user response：
Perhaps what you have in mind is something like this?
>>> df.groupby('time')['names'].value_counts(normalize=True).unstack().plot(kind='bar')
CodePudding user response：
import matplotlib.pyplot as plt
import seaborn as sns
df2 = df.groupby('time', as_index=False)['names'].value_counts(normalize=True)
sns.barplot(data=df2, x='time', y='proportion', hue='names')
plt.title('Proportion of Unique Values per Day')
plt.show()
Output:
Or, if you want to be really fancy:
import plotly.express as px
fig = px.bar(data_frame=df2, x='time', y='proportion', color='names', barmode='group')
fig.show()
Output:
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