Hey people of the web,

I have a function i'm creating 'show_monthly_temp', in which i'm attempting to plot a figure with 12 scatter sub plots, where each subplot's purpose mentioned in the next lines.

the function

which prints the following thing (DATE is a datetime object):

Now, what I attempt to do (unsuccessfully) is to plot 12 subplots representing the average of each month of the year throughout the years of sampling.

Any ideas of how I could do so?

Just to emphasise what i'm aiming for:

each subplot is a month, where Y will represent the temperature during that year of measurement, and X the year of the sample.

Thanks! :-)

full code is here:

import pandas as pd
from datetime import datetime
import numpy as np
import scipy as stats
import matplotlib.pyplot as plt

def show_monthly_temp(tmax):
    print(tmax)
    # getting averages for months
    # tmax_grouped_avg = tmax.groupby([tmax.index.month,tmax.index.year]).mean()
    tmax_grouped_avg = tmax.groupby(tmax.index.strftime("%m/%Y")).mean()
    print(type(tmax_grouped_avg))
    fig, axes = plt.subplots(6,2)
    print(tmax_grouped_avg)
    # tmax_grouped_avg = pd.to_datetime(tmax_grouped_avg)
    plt.show()


if __name__ == '__main__':
    df = pd.read_csv("2961941.csv")

    # set date column as index, drop the 'DATE' column to avoid repititions   create as datetime object
    # speed up parsing using infer_datetime_format=True.
    df.index = pd.to_datetime(df['DATE'], infer_datetime_format=True)

    # create new tables

    tmax = df.filter(['TMAX'], axis=1).dropna()
    snow = df.filter(['SNOW']).dropna()


    # count number of snow day samples - make sure at least >= 28
    snow_grouped = snow.groupby(pd.Grouper(level='DATE', freq="M")).transform('count')
    snow = (snow[snow_grouped['SNOW'] >= 28])

    # count number of tmax day samples - make sure at least >= 28
    tmax_grouped = tmax.groupby(pd.Grouper(level='DATE', freq="M")).transform('count')
    tmax = (tmax[tmax_grouped['TMAX'] >= 28])

    ################ Until here - initialized data ###############
    show_monthly_temp(tmax)

CodePudding user response：

If I understood correctly, you could do it like this:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import calendar

# Fake data - generating 'tmax_grouped_avg'.
df = pd.DataFrame({'Date': pd.date_range('1990-10-01', '2023-05-01')})
df['TMAX'] = np.random.random((len(df)))*30
tmax_grouped_avg = df.groupby(df['Date'].dt.strftime('%m/%Y')).mean()

# New code here.
tmax_grouped_avg['datetime'] = pd.to_datetime(tmax_grouped_avg.index)
tmax_grouped_avg['Year'] = tmax_grouped_avg['datetime'].dt.year
groups = tmax_grouped_avg.groupby(tmax_grouped_avg['datetime'].dt.month)

f, axes = plt.subplots(nrows=3, ncols=4, figsize=(12, 6))

for (grp_id, grp_df), ax in zip(groups, axes.ravel()):
    # Check if you want sharex and sharey.
    grp_df.plot.scatter(ax=ax, x='Year', y='TMAX', title=f'{calendar.month_name[grp_id]}', legend=False, 
                        sharey=True, sharex=True)

plt.suptitle('Maximum temperature for each month')
plt.tight_layout()
plt.show()

Result: