有没有办法防止dtype在重新索引/上采样时间序列时从Int64更改为float64？

Question

我正在使用pandas 0.17.0并且df与此类似:

df.head()
Out[339]: 
                       A     B  C
DATE_TIME                        
2016-10-08 13:57:00  in   5.61  1
2016-10-08 14:02:00  in   8.05  1
2016-10-08 14:07:00  in   7.92  0
2016-10-08 14:12:00  in   7.98  0
2016-10-08 14:17:00  out  8.18  0

df.tail()
Out[340]: 
                       A     B  C
DATE_TIME                        
2016-11-08 13:42:00  in   8.00  0
2016-11-08 13:47:00  in   7.99  0
2016-11-08 13:52:00  out  7.97  0
2016-11-08 13:57:00  in   8.14  1
2016-11-08 14:02:00  in   8.16  1

以下内容dtypes:

print (df.dtypes)
A     object
B    float64
C      int64
dtype: object

当我重新索引我df的分钟间隔时,所有列都会int64更改为float64.

index = pd.date_range(df.index[0], df.index[-1], freq="min") 
df2 = df.reindex(index)

print (df2.dtypes)
A     object
B    float64
C    float64
dtype: object

另外,如果我尝试重新采样

df3 = df.resample('Min')

这int64将成为一个float64由于某种原因我松开我的object专栏.

print (df3.dtypes)

print (df3.dtypes)
B    float64
C    float64
dtype: object

由于我希望在后续步骤(在将其df与另一个连接之后df)中根据这种区别对列进行不同的插值,因此我需要它们来维护它们的原始dtype.我的实际上df每种类型的列都多得多,因此我正在寻找一种不依赖于按标签单独调用列的解决方案.

有没有办法在dtype整个重建索引中保持它们？或者是否有一种方法可以在dtype以后分配它们(它们是除了NAN之外仅包含整数的唯一列)？有谁能够帮我？

Answer 1

这是不可能的,因为如果你NaN在某个列中得到至少一个值,int则转换为float.

index = pd.date_range(df.index[0], df.index[-1], freq="min") 
df2 = df.reindex(index)

print (df2)
                       A     B    C
2016-10-08 13:57:00   in  5.61  1.0
2016-10-08 13:58:00  NaN   NaN  NaN
2016-10-08 13:59:00  NaN   NaN  NaN
2016-10-08 14:00:00  NaN   NaN  NaN
2016-10-08 14:01:00  NaN   NaN  NaN
2016-10-08 14:02:00   in  8.05  1.0
2016-10-08 14:03:00  NaN   NaN  NaN
2016-10-08 14:04:00  NaN   NaN  NaN
2016-10-08 14:05:00  NaN   NaN  NaN
2016-10-08 14:06:00  NaN   NaN  NaN
2016-10-08 14:07:00   in  7.92  0.0
2016-10-08 14:08:00  NaN   NaN  NaN
2016-10-08 14:09:00  NaN   NaN  NaN
2016-10-08 14:10:00  NaN   NaN  NaN
2016-10-08 14:11:00  NaN   NaN  NaN
2016-10-08 14:12:00   in  7.98  0.0
2016-10-08 14:13:00  NaN   NaN  NaN
2016-10-08 14:14:00  NaN   NaN  NaN
2016-10-08 14:15:00  NaN   NaN  NaN
2016-10-08 14:16:00  NaN   NaN  NaN
2016-10-08 14:17:00  out  8.18  0.0

print (df2.dtypes)
A     object
B    float64
C    float64
dtype: object

但是,如果使用的参数fill_value中reindex,dtypes都没有改变:

index = pd.date_range(df.index[0], df.index[-1], freq="min") 
df2 = df.reindex(index, fill_value=0)

print (df2)
                       A     B  C
2016-10-08 13:57:00   in  5.61  1
2016-10-08 13:58:00    0  0.00  0
2016-10-08 13:59:00    0  0.00  0
2016-10-08 14:00:00    0  0.00  0
2016-10-08 14:01:00    0  0.00  0
2016-10-08 14:02:00   in  8.05  1
2016-10-08 14:03:00    0  0.00  0
2016-10-08 14:04:00    0  0.00  0
2016-10-08 14:05:00    0  0.00  0
2016-10-08 14:06:00    0  0.00  0
2016-10-08 14:07:00   in  7.92  0
2016-10-08 14:08:00    0  0.00  0
2016-10-08 14:09:00    0  0.00  0
2016-10-08 14:10:00    0  0.00  0
2016-10-08 14:11:00    0  0.00  0
2016-10-08 14:12:00   in  7.98  0
2016-10-08 14:13:00    0  0.00  0
2016-10-08 14:14:00    0  0.00  0
2016-10-08 14:15:00    0  0.00  0
2016-10-08 14:16:00    0  0.00  0
2016-10-08 14:17:00  out  8.18  0

print (df2.dtypes)
A     object
B    float64
C      int64
dtype: object

更好的是使用method='ffill在reindex:

index = pd.date_range(df.index[0], df.index[-1], freq="min") 
df2 = df.reindex(index, method='ffill')

print (df2)
                       A     B  C
2016-10-08 13:57:00   in  5.61  1
2016-10-08 13:58:00   in  5.61  1
2016-10-08 13:59:00   in  5.61  1
2016-10-08 14:00:00   in  5.61  1
2016-10-08 14:01:00   in  5.61  1
2016-10-08 14:02:00   in  8.05  1
2016-10-08 14:03:00   in  8.05  1
2016-10-08 14:04:00   in  8.05  1
2016-10-08 14:05:00   in  8.05  1
2016-10-08 14:06:00   in  8.05  1
2016-10-08 14:07:00   in  7.92  0
2016-10-08 14:08:00   in  7.92  0
2016-10-08 14:09:00   in  7.92  0
2016-10-08 14:10:00   in  7.92  0
2016-10-08 14:11:00   in  7.92  0
2016-10-08 14:12:00   in  7.98  0
2016-10-08 14:13:00   in  7.98  0
2016-10-08 14:14:00   in  7.98  0
2016-10-08 14:15:00   in  7.98  0
2016-10-08 14:16:00   in  7.98  0
2016-10-08 14:17:00  out  8.18  0

print (df2.dtypes)
A     object
B    float64
C      int64
dtype: object

如果使用resample,您可以A通过unstack和返回列stack,但不幸的是仍然存在以下问题float:

df3 = df.set_index('A', append=True)
        .unstack()
        .resample('Min', fill_method='ffill')
        .stack()
        .reset_index(level=1)
print (df3)
                       A     B    C
DATE_TIME                          
2016-10-08 13:57:00   in  5.61  1.0
2016-10-08 13:58:00   in  5.61  1.0
2016-10-08 13:59:00   in  5.61  1.0
2016-10-08 14:00:00   in  5.61  1.0
2016-10-08 14:01:00   in  5.61  1.0
2016-10-08 14:02:00   in  8.05  1.0
2016-10-08 14:03:00   in  8.05  1.0
2016-10-08 14:04:00   in  8.05  1.0
2016-10-08 14:05:00   in  8.05  1.0
2016-10-08 14:06:00   in  8.05  1.0
2016-10-08 14:07:00   in  7.92  0.0
2016-10-08 14:08:00   in  7.92  0.0
2016-10-08 14:09:00   in  7.92  0.0
2016-10-08 14:10:00   in  7.92  0.0
2016-10-08 14:11:00   in  7.92  0.0
2016-10-08 14:12:00   in  7.98  0.0
2016-10-08 14:13:00   in  7.98  0.0
2016-10-08 14:14:00   in  7.98  0.0
2016-10-08 14:15:00   in  7.98  0.0
2016-10-08 14:16:00   in  7.98  0.0
2016-10-08 14:17:00  out  8.18  0.0

print (df3.dtypes)
A     object
B    float64
C    float64
dtype: object

我尝试修改以前的答案,以转换为`int:

int_cols = df.select_dtypes(['int64']).columns
print (int_cols)
Index(['C'], dtype='object')

index = pd.date_range(df.index[0], df.index[-1], freq="s")
df2 = df.reindex(index)

for col in df2:
    if col == int_cols: 
        df2[col].ffill(inplace=True)
        df2[col] = df2[col].astype(int)
    elif df2[col].dtype == float:
        df2[col].interpolate(inplace=True)
    else:
        df2[col].ffill(inplace=True)

#print (df2)

print (df2.dtypes)
A     object
B    float64
C      int32
dtype: object