python - 有没有办法在重新索引/上采样时间序列时防止 dtype 从 Int64 更改为 float64？

Question

我正在使用 pandas 0.17.0 并且有一个类似于这个的 df:

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

具有以下dtypes:

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

当我将 df 重新索引为分钟间隔时，所有列 int64 都会更改为 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

此外，如果我尝试重新采样

df3 = df.resample('Min')

int64 将变成 float64 并且出于某种原因我丢失了我的 object 列。

打印(df3.dtypes)

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

因为我想在后续步骤中根据这种区别对列进行不同的插值(在将 df 与另一个 df 连接之后)，我需要它们保持原来的状态数据类型。我真正的 df 有更多的每种类型的列，因此我正在寻找一种不依赖于通过标签单独调用列的解决方案。

有没有办法在重建索引的过程中维护它们的dtype？或者有没有一种方法可以在之后为它们分配它们的 dtype (它们是除了 NAN 之外唯一仅由整数组成的列)？ 谁能帮帮我？

Answer 1

是impossible ，因为如果您在某列中获得至少一个 NaN 值，int 将转换为 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

但是如果在reindex中使用参数fill_value , dtypes 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

更好的方法是在 reindex 中使用 method='ffill:

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 ，您可以通过 unstack 返回 A 列和 stack , 但不幸的是 float 仍然存在问题:

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

我尝试修改之前的answer用于转换为 `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