我有这个数据框:
startTime endTime emails_received
index
2014-01-24 14:00:00 1390568400 1390569600 684
2014-01-24 14:00:00 1390568400 1390569300 700
2014-01-24 14:05:00 1390568700 1390569300 438
2014-01-24 14:05:00 1390568700 1390569900 586
2014-01-24 16:00:00 1390575600 1390576500 752
2014-01-24 16:00:00 1390575600 1390576500 743
2014-01-24 16:00:00 1390575600 1390576500 672
2014-01-24 16:00:00 1390575600 1390576200 712
2014-01-24 16:00:00 1390575600 1390576800 708
我运行 resample("10min",how="median").dropna() 并得到:
startTime endTime emails_received
start
2014-01-24 14:00:00 1390568550 1390569450 635
2014-01-24 16:00:00 1390575600 1390576500 712
这是正确的。有什么方法可以通过 pandas 轻松获得平均值的标准差吗？
最佳答案
您只需要在您的 DataFrame 上调用 .std()。这是一个说明性示例。
创建一个DatetimeIndex
In [38]: index = pd.DatetimeIndex(start='2000-1-1',freq='1T', periods=1000)
创建一个包含 2 列的 DataFrame
In [45]: df = pd.DataFrame({'a':range(1000), 'b':range(1000,3000,2)}, index=index)
DataFrame 的 Head、Std 和 Mean
In [47]: df.head()
Out[47]:
a b
2000-01-01 00:00:00 0 1000
2000-01-01 00:01:00 1 1002
2000-01-01 00:02:00 2 1004
2000-01-01 00:03:00 3 1006
2000-01-01 00:04:00 4 1008
In [48]: df.std()
Out[48]:
a 288.819436
b 577.638872
dtype: float64
In [49]: df.mean()
Out[49]:
a 499.5
b 1999.0
dtype: float64
下采样并执行计算相同的统计分数
In [54]: df = df.resample(rule="10T",how="median")
In [55]: df
Out[55]:
DatetimeIndex: 100 entries, 2000-01-01 00:00:00 to 2000-01-01 16:30:00
Freq: 10T
Data columns (total 2 columns):
a 100 non-null values
b 100 non-null values
dtypes: float64(1), int64(1)
In [56]: df.head()
Out[56]:
a b
2000-01-01 00:00:00 4.5 1009
2000-01-01 00:10:00 14.5 1029
2000-01-01 00:20:00 24.5 1049
2000-01-01 00:30:00 34.5 1069
2000-01-01 00:40:00 44.5 1089
In [57]: df.std()
Out[57]:
a 290.11492
b 580.22984
dtype: float64
In [58]: df.mean()
Out[58]:
a 499.5
b 1999.0
dtype: float64
通过std()进行下采样
In [62]: df2 = df.resample(rule="10T", how=np.std)
In [63]: df2
Out[63]:
DatetimeIndex: 100 entries, 2000-01-01 00:00:00 to 2000-01-01 16:30:00
Freq: 10T
Data columns (total 2 columns):
a 100 non-null values
b 100 non-null values
dtypes: float64(2)
In [64]: df2.head()
Out[64]:
a b
2000-01-01 00:00:00 3.02765 6.055301
2000-01-01 00:10:00 3.02765 6.055301
2000-01-01 00:20:00 3.02765 6.055301
2000-01-01 00:30:00 3.02765 6.055301
2000-01-01 00:40:00 3.02765 6.055301
以下是 .std() 方法的文档字符串中的信息。
Return standard deviation over requested axis.
NA/null values are excluded
Parameters
----------
axis : {0, 1}
0 for row-wise, 1 for column-wise
skipna : boolean, default True
Exclude NA/null values. If an entire row/column is NA, the result
will be NA
level : int, default None
If the axis is a MultiIndex (hierarchical), count along a
particular level, collapsing into a DataFrame
Returns
-------
std : Series (or DataFrame if level specified)
Normalized by N-1 (unbiased estimator).
关于python - Pandas - 重采样和标准差，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/21480041/