计算pandas中数字和非数字列的每日平均值(Compute daily averages of numeric and non

计算pandas中数字和非数字列的每日平均值(Compute daily averages of numeric and non-numeric columns in pandas)

我有一个每小时时间索引的数据框：

wind_direction relative_humidity dates 2017-07-18 19:00:00 W 88 2017-07-18 20:00:00 N 88 2017-07-18 21:00:00 W 90 2017-07-18 22:00:00 S 91 2017-07-18 23:00:00 W 93

如何计算每日平均值，使得对于数字列，我们计算每日平均值，对于非数字列，我们输出出现次数最多的值。

- 编辑：

我这样做了：

df = df.resample('D').mean()

但是这会返回错误

I have a dataframe with hourly time index:

wind_direction relative_humidity dates 2017-07-18 19:00:00 W 88 2017-07-18 20:00:00 N 88 2017-07-18 21:00:00 W 90 2017-07-18 22:00:00 S 91 2017-07-18 23:00:00 W 93

How can I compute daily average such that for numeric columns we compute daily mean and for non-numeric columns we output the value which occurs most number of times.

-- EDIT:

I did this:

df = df.resample('D').mean()

However this returns an error

最满意答案

选项1

from cytoolz.dicttoolz import merge ncols = df.select_dtypes([np.number]).columns ocols = df.columns.difference(ncols) df.index = pd.to_datetime(df.index) d = merge( {c: 'mean' for c in ncols}, {c: lambda x: pd.value_counts(x).index[0] for c in ocols} ) df.resample('D').agg(d) relative_humidity wind_direction dates 2017-07-18 90 W ​

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选项2

df.index = pd.to_datetime(df.index) g = df.resample('D') g.mean().combine_first(g.agg(lambda x: pd.value_counts(x).index[0]))[df.columns] relative_humidity wind_direction dates 2017-07-18 90 W

Option 1

from cytoolz.dicttoolz import merge ncols = df.select_dtypes([np.number]).columns ocols = df.columns.difference(ncols) df.index = pd.to_datetime(df.index) d = merge( {c: 'mean' for c in ncols}, {c: lambda x: pd.value_counts(x).index[0] for c in ocols} ) df.resample('D').agg(d) relative_humidity wind_direction dates 2017-07-18 90 W ​

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Option 2

df.index = pd.to_datetime(df.index) g = df.resample('D') g.mean().combine_first(g.agg(lambda x: pd.value_counts(x).index[0]))[df.columns] relative_humidity wind_direction dates 2017-07-18 90 W