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I extracted the following data from a dataframe . https://i.imgur.com/rCLfV83.jpg

The question is, how do I plot a graph, probably a histogram type, where the horizontal axis are the hours as bins [16:00 17:00 18:00 ...24:00] and the bars are the average rainfall during each of those hours.

I just don't know enough pandas yet to get this off the ground so I need some help. Sample data below as requested. 

Date        Hours  `Precip`

1996-07-30  21        1

1996-08-17  16        1
            18        1
1996-08-30  16        1
            17        1
            19        5
            22        1
1996-09-30  19        5
            20        5
1996-10-06  20        1
            21        1
1996-10-19  18        4
1996-10-30  19        1
1996-11-05  20        3
1996-11-16  16        1
            19        1
1996-11-17  16        1
1996-11-29  16        1
1996-12-04  16        9
            17       27
            19        1
1996-12-12  19        1
1996-12-30  19       10
            22        1
1997-01-18  20        1
ioio
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  • Hi, can you please provide us with some data to replicate? This way you would receive more answers. Check [why shouldnt you post just an image in a question](https://meta.stackoverflow.com/questions/285551/why-not-upload-images-of-code-on-so-when-asking-a-question) – anky Jan 27 '19 at 04:24
  • I would provide data in more friendly form but I don't know how to format it so that it doesn't end up on just one line. :( – ioio Jan 27 '19 at 05:01

2 Answers2

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It seems df is a multi-index DataFrame after a groupby.

Transform the index to a DatetimeIndex

date_hour_idx = df.reset_index()[['Date', 'Hours']] \
                    .apply(lambda x: '{} {}:00'.format(x['Date'], x['Hours']), axis=1)
precip_series = df.reset_index()['Precip']
precip_series.index = pd.to_datetime(date_hour_idx)

Resample to hours using 'H'

# This will show NaN for hours without an entry
resampled_nan = precip_series.resample('H').asfreq()

# This will fill NaN with 0s
resampled_fillna = precip_series.resample('H').asfreq().fillna(0)

If you want this to be the mean per hour, change your groupby(...).sum() to groupby(...).mean()

You can resample to other intervals too -> pandas resample documentation

More about resampling the DatetimeIndex -> https://pandas.pydata.org/pandas-docs/stable/reference/resampling.html

kentwait
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  • I just figured it out too :) I did want the mean per hour so I used groupby, as you also suggested. No need for resampling. All I had to do was write this code and no more: hourly_rain_summed.groupby('Hours').mean() – ioio Jan 27 '19 at 09:55
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It seems to be easy when you have data. I generate artificial data by Pandas for this example:

import pandas as pd
import radar
import random

'''>>>   date'''
r2 =()
for a in range(1,51):
    t= (str(radar.random_datetime(start='1985-05-01', stop='1985-05-04')),)
    r2 = r2 + t

r3 =list(r2)
r3.sort()
#print(r3)
'''>>>  variable'''

x = [random.randint(0,16) for x in range(50)]
df= pd.DataFrame({'date': r3, 'measurement': x})
print(df)
'''order'''
col1 = df.join(df['date'].str.partition(' ')[[0,2]]).rename({0: 'daty', 2: 'godziny'}, axis=1)

col2 = df['measurement'].rename('pomiary')
p3 = pd.concat([col1, col2], axis=1, sort=False)

p3 = p3.drop(['measurement'], axis=1)
p3 = p3.drop(['date'], axis=1)

Time for sum and plot:

dx = p3.groupby(['daty']).mean()
print(dx)
import matplotlib.pyplot as plt
dx.plot.bar()
plt.show()

Plot of the mean measurements

Wojciech Moszczyński
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