Load the data:
df = pd.read_csv('wind_data.csv')
Convert date
to datetime
and set as the index
df.date = pd.to_datetime(df.date)
df.set_index('date', drop=True, inplace=True)
Create a DateFrame
for 1961
df_1961 = df[df.index < pd.to_datetime('1962-01-01')]
Resample for statistical calculations
df_1961.resample('W').mean()
df_1961.resample('W').min()
df_1961.resample('W').max()
df_1961.resample('W').std()
Plot the data for 1961:
fix, axes = plt.subplots(12, 1, figsize=(15, 60), sharex=True)
for name, ax in zip(df_1961.columns, axes):
ax.plot(df_1961[name], label='Daily')
ax.plot(df_1961_mean[name], label='Weekly Mean Resample')
ax.plot(df_1961_min[name], label='Weekly Min')
ax.plot(df_1961_max[name], label='Weekly Max')
ax.set_title(name)
ax.legend()
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