熊猫日均值，pandas.resample
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pandas daily average, pandas.resample
提问by OGMGIC
I have a csv file similar to this
我有一个与此类似的 csv 文件
Date,Temp1,Temp2
23-Oct-09 01:00:00,21.1,22.3
23-Oct-09 04:00:00,22.3,23.8
23-Oct-09 07:00:00,21.4,21.3
23-Oct-09 10:00:00,21.5,21.6
23-Oct-09 13:00:00,22.3,23.8
23-Oct-09 16:00:00,21.4,21.3
23-Oct-09 19:00:00,21.1,22.3
23-Oct-09 22:00:00,21.4,21.3
24-Oct-09 01:00:00,22.3,23.8
24-Oct-09 04:00:00,22.3,23.8
24-Oct-09 07:00:00,21.1,22.3
24-Oct-09 10:00:00,22.3,23.8
24-Oct-09 13:00:00,21.1,22.3
24-Oct-09 16:00:00,22.3,23.8
24-Oct-09 19:00:00,21.1,22.3
24-Oct-09 22:00:00,22.3,23.8
I have read the data with:
我已经阅读了以下数据：
df=pd.read_csv('data.csv', index_col=0)
and converted the index to date time
并将索引转换为日期时间
df.index=pd.to_datetime(df.index)
Now I want to take the mean of each daily temperature, I have been trying to use pd.resample as below, but have been receiving errors. I've read the pandas.resample docs and numerous examples on here and am still at a loss...
现在我想取每个每日温度的平均值，我一直在尝试使用 pd.resample 如下，但一直收到错误。我已经阅读了 pandas.resample 文档和这里的大量示例，但仍然不知所措......
df_avg = df.resample('D', how = 'mean')
DataError: No numeric types to aggregate
DataError：没有要聚合的数字类型
I would like df_avg to be a dataframe with a datetime index and the two 2 columns. I am using pandas 0.17.1 and python 3.5.2, any help greatly appreciated!
我希望 df_avg 是一个带有日期时间索引和两个 2 列的数据框。我正在使用 Pandas 0.17.1 和 python 3.5.2，非常感谢任何帮助！
回答by jezrael
You need convert stringcolumns to floatfirst:
您需要先将string列转换为float：
#add parameter parse_dates for convert to datetime first column
df=pd.read_csv('data.csv', index_col=0, parse_dates=[0])
df['Temp1'] = df.Temp1.astype(float)
df['Temp2'] = df.Temp2.astype(float)
df_avg = df.resample('D').mean()
If astypereturn error, problem is there are some non numeric values. So you need use to_numericwith errors='coerce'- then all 'problematic' values are converted to NaN:
如果astypereturn error，问题是有一些非数字值。所以你需要使用to_numericwith errors='coerce'- 然后所有“有问题”的值都转换为NaN：
df['Temp1'] = pd.to_numeric(df.Temp1, errors='coerce')
df['Temp2'] = pd.to_numeric(df.Temp2, errors='coerce')
You can also check all rows with problematic values with boolean indexing:
您还可以使用以下命令检查具有问题值的所有行boolean indexing：
print df[pd.to_numeric(df.Temp1, errors='coerce').isnull()]
print df[pd.to_numeric(df.Temp2, errors='coerce').isnull()]
