9 FAQ

9.1 How to resample by year, but have it end in September?

This is called anchored offset. One possible use to it is to calculate statistics according to the hydrological year that, for example, ends in September.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.dates import DateFormatter
import matplotlib.dates as mdates
import matplotlib.ticker as ticker
import seaborn as sns
sns.set(style="ticks", font_scale=1.5)  # white graphs, with large and legible letters

filename = "../archive/data/Kinneret_Kvuza_daily_rainfall.csv"
df = pd.read_csv(filename, na_values=['-'])
df.rename(columns={'Date': 'date',
                   'Daily Rainfall (mm)': 'rain'}, inplace=True)
df['date'] = pd.to_datetime(df['date'], dayfirst=True)
df = df.set_index('date')
df = df.resample('D').asfreq().fillna(0)  # asfreq = replace
df

	Station	rain
date
1980-01-02	Kinneret Kvuza 09/1977-08/2023	0.0
1980-01-03	0	0.0
1980-01-04	0	0.0
1980-01-05	Kinneret Kvuza 09/1977-08/2023	35.5
1980-01-06	Kinneret Kvuza 09/1977-08/2023	2.2
...	...	...
2019-12-26	Kinneret Kvuza 09/1977-08/2023	39.4
2019-12-27	Kinneret Kvuza 09/1977-08/2023	5.2
2019-12-28	Kinneret Kvuza 09/1977-08/2023	1.6
2019-12-29	0	0.0
2019-12-30	Kinneret Kvuza 09/1977-08/2023	0.1

14608 rows × 2 columns

fig, ax = plt.subplots(2,1)
ax[0].plot(df['rain'], color='black')
ax[1].plot(df.loc['1998':'2000', 'rain'], color='black')
locator = mdates.AutoDateLocator(minticks=4, maxticks=8)
formatter = mdates.ConciseDateFormatter(locator)
ax[1].xaxis.set_major_locator(locator)
ax[1].xaxis.set_major_formatter(formatter)
fig.text(0.02, 0.5, 'daily precipitation (mm)', va='center', rotation='vertical')
ax[0].set_title("Kvutzat Kinneret")

Text(0.5, 1.0, 'Kvutzat Kinneret')

We see a marked dry season during the summer, so let’s assume the Hydrological Year ends in September.

df_year = df.resample('A-SEP').sum()
df_year = df_year.loc['1980':'2003']
df_year

/var/folders/c3/7hp0d36n6vv8jc9hm2440__00000gn/T/ipykernel_94063/2047090134.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.
  df_year = df.resample('A-SEP').sum()

	rain
date
1980-09-30	355.5
1981-09-30	463.1
1982-09-30	221.7
1983-09-30	557.1
1984-09-30	335.3
1985-09-30	379.8
1986-09-30	300.7
1987-09-30	424.7
1988-09-30	421.6
1989-09-30	251.6
1990-09-30	432.5
1991-09-30	328.3
1992-09-30	738.4
1993-09-30	434.9
1994-09-30	255.4
1995-09-30	408.6
1996-09-30	373.0
1997-09-30	416.2
1998-09-30	451.9
1999-09-30	227.8
2000-09-30	378.9
2001-09-30	273.9
2002-09-30	445.2
2003-09-30	602.4

fig, ax = plt.subplots()
ax.bar(df_year.index, df_year['rain'], color='black',
       width=365)
ax.set_ylabel("yearly precipitation (mm)")
ax.set_title("Kvutzat Kinneret")

Text(0.5, 1.0, 'Kvutzat Kinneret')

9.2 When upsampling, how to fill missing values with zero?

We did that in the example above, like this:

df = df.resample('D').asfreq().fillna(0)  # asfreq = replace