CODEWITHSUNDEEP
pandasdatetimematplotlibpython-datetime
KeyBarreto
KeyBarreto

Reputation: 9

How can I draw Yearly series using monthly data from a DateTimeIndex in Matplotlib?

I have monthly data of 6 variables from 2014 until 2018 in one dataset. I'm trying to draw 6 subplots (one for each variable) with monthly X axis (Jan, Feb....) and 5 series (one for each year) with their legend.

This is part of the data: Preview of the data

I created 5 series (one for each year) per variable (30 in total) and I'm getting the expected output but using MANY lines of code.

What is the best way to achieve this using less lines of code?

This is an example how I created the series:

CL2014 = data_total['Charity Lottery'].where(data_total['Date'].dt.year == 2014)[0:12]

CL2015 = data_total['Charity Lottery'].where(data_total['Date'].dt.year == 2015)[12:24]

This is an example of how I'm plotting the series: axCL.plot(xvals, CL2014)

axCL.plot(xvals, CL2015)

axCL.plot(xvals, CL2016)

axCL.plot(xvals, CL2017)

axCL.plot(xvals, CL2018)

Upvotes: 0

Views: 280

Answers (2)

Code Different
Code Different

Reputation: 93151

There's no need to litter your namespace with 30 variables. Seaborn makes the job very easy but you need to normalize your dataframe first. This is what "normalized" or "unpivoted" looks like (Seaborn calls this "long form"):

Date        variable         value
2014-01-01  Charity Lottery  ...
2014-01-01  Racecourse       ...
2014-04-01  Bingo Halls      ...
2014-04-01  Casino           ...

Your screenshot is a "pivoted" or "wide form" dataframe.

df_plot = pd.melt(df, id_vars='Date')
df_plot['Year'] = df_plot['Date'].dt.year
df_plot['Month'] = df_plot['Date'].dt.strftime('%b')

import seaborn as sns
plot = sns.catplot(data=df_plot, x='Month', y='value',
                   row='Year', col='variable', kind='bar',
                   sharex=False)
plot.savefig('figure.png', dpi=300)

Result (all numbers are randomly generated):

Seaborn result

Upvotes: 1

Matt
Matt

Reputation: 1

I would try using .groupby(), it is really powerful for parsing down things like this:

for _, group in data_total.groupby([year, month])[[x_variable, y_variable]]:
    plt.plot(group[x_variables], group[y_variables])

So here the groupby will separate your data_total DataFrame into year/month subsets, with the [[]] on the end to parse it down to the x_variable (assuming it is in your data_total DataFrame) and your y_variable, which you can make any of those features you are interested in.

I would decompose your datetime column into separate year and month columns, then use those new columns inside that groupby as the [year, month]. You might be able to pass in the dt.year and dt.month like you had before... not sure, try it both ways!

Upvotes: 0

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