重采样数据框以添加缺失的日期。

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英文:

Resample dataframe to add missing dates

问题

我有一个包含多个字符串列、一个日期列和一个整数值列的数据框。

我想要为每组文本列“ffill”缺失的日期。缺失的日期是数据框中最小日期到最大日期的所有日期。我认为使用示例更容易理解。

示例输入：

  1. group        rtype     location   hardware     date          value
  2. my-group     type-s     NY         DTop        2020-08-05     10
  3. my-group     type-s     NY         DTop        2020-08-07     20
  4. my-group     type-s     NY         DTop        2020-08-10     30
  5. my-group     type-s     NY         Tower       2020-08-01     40
  6. my-group     type-s     NY         Tower       2020-08-07     50
  7. ot-group     type-t     NY         LTop        2020-08-08     90

此数据框的最小和最大日期：（起始日期）2020-08-01 -（结束日期）2020-08-10

示例输出：

  1. group       rtype       location   hardware    date          value
  2. my-group    type-s        NY          DTop     2020-08-01    0
  3. my-group    type-s        NY          DTop     2020-08-02    0
  4. my-group    type-s        NY          DTop     2020-08-03    0
  5. my-group    type-s        NY          DTop     2020-08-04    0
  6. my-group    type-s        NY          DTop     2020-08-05    10
  7. my-group    type-s        NY          DTop     2020-08-06    10
  8. my-group    type-s        NY          DTop     2020-08-07    20
  9. my-group    type-s        NY          DTop     2020-08-08    20
  10. my-group    type-s        NY          DTop     2020-08-09    20
  11. my-group    type-s        NY          DTop     2020-08-10    30
  12. my-group    type-s        NY          Tower    2020-08-01    40
  13. my-group    type-s        NY          Tower    2020-08-02    40
  14. my-group    type-s        NY          Tower    2020-08-03    40
  15. my-group    type-s        NY          Tower    2020-08-04    40
  16. my-group    type-s        NY          Tower    2020-08-05    40
  17. my-group    type-s        NY          Tower    2020-08-06    40
  18. my-group    type-s        NY          Tower    2020-08-07    50
  19. my-group    type-s        NY          Tower    2020-08-08    50
  20. my-group    type-s        NY          Tower    2020-08-09    50
  21. my-group    type-s        NY          Tower    2020-08-10    50
  22. ot-group    type-t        NY          LTop     2020-08-01    0
  23. ot-group    type-t        NY          LTop     2020-08-02    0
  24. ot-group    type-t        NY          LTop     2020-08-03    0
  25. ot-group    type-t        NY          LTop     2020-08-04    0
  26. ot-group    type-t        NY          LTop     2020-08-05    0
  27. ot-group    type-t        NY          LTop     2020-08-06    0
  28. ot-group    type-t        NY          LTop     2020-08-07    0
  29. ot-group    type-t        NY          LTop     2020-08-08    90
  30. ot-group    type-t        NY          LTop     2020-08-09    90
  31. ot-group    type-t        NY          LTop     2020-08-10    90

在这个示例中，我将位置保持不变，以避免额外的长输出。

我可以使用pd.date_range()来获取所需的日期。

我尝试使用多索引的resample，但遇到了错误（类似于此问题）。

我尝试了此答案中提到的方法，但似乎不起作用。

以下是我使用的代码：

  1. import pandas as pd
  3. df = pd.read_csv('data.csv')
  4. df.set_index('date', inplace=True)
  5. date_range = pd.date_range(df.index.min(), df.index.max(), freq='D')
  7. print(len(date_range), date_range)
  8. def reindex_by_date(df):    
  9.     return df.reindex(date_range).ffill()
  11. df = df.groupby(['group','rtype','location','hardware']).apply(reindex_by_date).reset_index([0,1,2,3], drop=True)
  12. print(df.to_string())

此代码的输出：

  1. 10 DatetimeIndex(['2020-08-01', '2020-08-02', '2020-08-03', '2020-08-04',
  2.                '2020-08-05', '2020-08-06', '2020-08-07', '2020-08-08',
  3.                '2020-08-09', '2020-08-10'],
  4.               dtype='datetime64[ns]', freq='D')
  5.             group  rtype  location  hardware  value
  6. 2020-08-01    NaN    NaN       NaN       NaN    NaN
  7. 2020-08-02    NaN    NaN       NaN       NaN    NaN
  8. 2020-08-03    NaN    NaN       NaN       NaN    NaN
  9. 2020-08-04    NaN    NaN       NaN       NaN    NaN
  10. 2020-08-05    NaN    NaN       NaN       NaN    NaN
  11. 2020-08-06    NaN    NaN       NaN       NaN    NaN
  12. 2020-08-07    NaN    NaN       NaN       NaN    NaN
  13. 2020-08-08    NaN    NaN       NaN       NaN    NaN
  14. 2020-08-09    NaN    NaN       NaN       NaN    NaN
  15. 2020-08-10    NaN    NaN       NaN       NaN    NaN
  16. 2020-08-01    NaN    NaN       NaN       NaN    NaN
  17. 2020-08-02    NaN    NaN       NaN       NaN    NaN
  18. 2020-08-03    NaN    NaN       NaN       NaN    NaN
  19. 2020-08-04    NaN    NaN      
  21. <details>
  22. <summary>英文:</summary>
  24. I have a dataframe with multiple string columns, one date column and one int value column.
  26. I want to `ffill` the missing dates for each group of text columns. The missing dates are all dates from the min date to max date in the dataframe. I think this is better explained using an example.
  28. Sample Input:

group rtype location hardware date value
my-group type-s NY DTop 2020-08-05 10
my-group type-s NY DTop 2020-08-07 20
my-group type-s NY DTop 2020-08-10 30
my-group type-s NY Tower 2020-08-01 40
my-group type-s NY Tower 2020-08-07 50
ot-group type-t NY LTop 2020-08-08 90

  1. Min and Max date for this dataframe: (start_date) 2020-08-01 - (end_date) 2020-08-10
  3. Sample Output:

group rtype location hardware date value
my-group type-s NY DTop 2020-08-01 0
my-group type-s NY DTop 2020-08-02 0
my-group type-s NY DTop 2020-08-03 0
my-group type-s NY DTop 2020-08-04 0
my-group type-s NY DTop 2020-08-05 10
my-group type-s NY DTop 2020-08-06 10
my-group type-s NY DTop 2020-08-07 20
my-group type-s NY DTop 2020-08-08 20
my-group type-s NY DTop 2020-08-09 20
my-group type-s NY DTop 2020-08-10 30
my-group type-s NY Tower 2020-08-01 40
my-group type-s NY Tower 2020-08-02 40
my-group type-s NY Tower 2020-08-03 40
my-group type-s NY Tower 2020-08-04 40
my-group type-s NY Tower 2020-08-05 40
my-group type-s NY Tower 2020-08-06 40
my-group type-s NY Tower 2020-08-07 50
my-group type-s NY Tower 2020-08-08 50
my-group type-s NY Tower 2020-08-09 50
my-group type-s NY Tower 2020-08-10 50
ot-group type-t NY LTop 2020-08-01 0
ot-group type-t NY LTop 2020-08-02 0
ot-group type-t NY LTop 2020-08-03 0
ot-group type-t NY LTop 2020-08-04 0
ot-group type-t NY LTop 2020-08-05 0
ot-group type-t NY LTop 2020-08-06 0
ot-group type-t NY LTop 2020-08-07 0
ot-group type-t NY LTop 2020-08-08 90
ot-group type-t NY LTop 2020-08-09 90
ot-group type-t NY LTop 2020-08-10 90

  2. In this example, I kept the location fixed to avoid an extra long output.
  3. I am able to get the dates I want using `pd.date_range()`.
  5. I tried using `resample` with multiindex but I run into errors (similar to [this](https://stackoverflow.com/questions/15799162/resampling-within-a-pandas-multiindex)).
  7. I tried the approach mentioned in [this answer](https://stackoverflow.com/a/32275705/1872234) but it doesn&#39;t seem to work:
  9. My code using:

import pandas as pd

df = pd.read_csv('data.csv')
df.set_index('date', inplace=True)
date_range = pd.date_range(df.index.min(), df.index.max(), freq='D')

print(len(date_range), date_range)
def reindex_by_date(df):
return df.reindex(date_range).ffill()

df = df.groupby(['group','rtype','location','hardware']).apply(reindex_by_date).reset_index([0,1,2,3], drop=True)
print(df.to_string())

  2. Output of this code:

10 DatetimeIndex(['2020-08-01', '2020-08-02', '2020-08-03', '2020-08-04',
'2020-08-05', '2020-08-06', '2020-08-07', '2020-08-08',
'2020-08-09', '2020-08-10'],
dtype='datetime64[ns]', freq='D')
group rtype location hardware value
2020-08-01 NaN NaN NaN NaN NaN
2020-08-02 NaN NaN NaN NaN NaN
2020-08-03 NaN NaN NaN NaN NaN
2020-08-04 NaN NaN NaN NaN NaN
2020-08-05 NaN NaN NaN NaN NaN
2020-08-06 NaN NaN NaN NaN NaN
2020-08-07 NaN NaN NaN NaN NaN
2020-08-08 NaN NaN NaN NaN NaN
2020-08-09 NaN NaN NaN NaN NaN
2020-08-10 NaN NaN NaN NaN NaN
2020-08-01 NaN NaN NaN NaN NaN
2020-08-02 NaN NaN NaN NaN NaN
2020-08-03 NaN NaN NaN NaN NaN
2020-08-04 NaN NaN NaN NaN NaN
2020-08-05 NaN NaN NaN NaN NaN
2020-08-06 NaN NaN NaN NaN NaN
2020-08-07 NaN NaN NaN NaN NaN
2020-08-08 NaN NaN NaN NaN NaN
2020-08-09 NaN NaN NaN NaN NaN
2020-08-10 NaN NaN NaN NaN NaN
2020-08-01 NaN NaN NaN NaN NaN
2020-08-02 NaN NaN NaN NaN NaN
2020-08-03 NaN NaN NaN NaN NaN
2020-08-04 NaN NaN NaN NaN NaN
2020-08-05 NaN NaN NaN NaN NaN
2020-08-06 NaN NaN NaN NaN NaN
2020-08-07 NaN NaN NaN NaN NaN
2020-08-08 NaN NaN NaN NaN NaN
2020-08-09 NaN NaN NaN NaN NaN
2020-08-10 NaN NaN NaN NaN NaN

  2. Can someone help please?
  4. **EDIT:**
  5. After fixing the DatetimeIndex issue, and using `fillna(0)`:

df = pd.read_csv('data.csv', parse_dates=['date'])
df.set_index('date', inplace=True)
date_range = pd.date_range(df.index.min(), df.index.max(), freq='D')

print(len(date_range), date_range)
def reindex_by_date(df):
return df.reindex(date_range).ffill().fillna(0)

df = df.groupby(['group','rtype','location','hardware']).apply(reindex_by_date).reset_index([0,1,2,3], drop=True).reset_index().rename(columns={'index': 'date'})

print(df.to_string())

  2. Output:
  1.            group   rtype location hardware  value

2020-08-01 0 0 0 0 0.0
2020-08-02 0 0 0 0 0.0
2020-08-03 0 0 0 0 0.0
2020-08-04 0 0 0 0 0.0
2020-08-05 my-group type-s NY DTop 10.0
2020-08-06 my-group type-s NY DTop 10.0
2020-08-07 my-group type-s NY DTop 20.0
2020-08-08 my-group type-s NY DTop 20.0
2020-08-09 my-group type-s NY DTop 20.0
2020-08-10 my-group type-s NY DTop 30.0
2020-08-01 my-group type-s NY Tower 40.0
2020-08-02 my-group type-s NY Tower 40.0
2020-08-03 my-group type-s NY Tower 40.0
2020-08-04 my-group type-s NY Tower 40.0
2020-08-05 my-group type-s NY Tower 40.0
2020-08-06 my-group type-s NY Tower 40.0
2020-08-07 my-group type-s NY Tower 50.0
2020-08-08 my-group type-s NY Tower 50.0
2020-08-09 my-group type-s NY Tower 50.0
2020-08-10 my-group type-s NY Tower 50.0
2020-08-01 0 0 0 0 0.0
2020-08-02 0 0 0 0 0.0
2020-08-03 0 0 0 0 0.0
2020-08-04 0 0 0 0 0.0
2020-08-05 0 0 0 0 0.0
2020-08-06 0 0 0 0 0.0
2020-08-07 0 0 0 0 0.0
2020-08-08 ot-group type-t NY LTop 90.0
2020-08-09 ot-group type-t NY LTop 90.0
2020-08-10 ot-group type-t NY LTop 90.0

  2. </details>
  5. # 答案1
  6. **得分**: 3
  8. 你忘记将`date`列转换为`DatetimeIndex`：
  10. ```python
  11. df = pd.read_csv('data.csv', parse_dates=['date'])
  12. # 在此处添加你的代码

另一种方法是使用pivot_table：

  1. # 如果你使用了 pd.read_csv(..., parse_dates=['date']) 则不需要以下行
  2. df['date'] = pd.to_datetime(df['date'])
  3. dti = pd.date_range(df['date'].min(), df['date'].max(), freq='D')
  4. cols = ['group', 'rtype', 'location', 'hardware']
  5. out = (df.pivot_table(index='date', columns=cols, values='value')
  6.          .reindex(dti.rename('date')).ffill().fillna(0)
  7.          .unstack().rename('value').reset_index())

输出：

  1. >>> out
  2.        group   rtype location hardware       date  value
  3. 0   my-group  type-s       NY     DTop 2020-08-01    0.0
  4. 1   my-group  type-s       NY     DTop 2020-08-02    0.0
  5. 2   my-group  type-s       NY     DTop 2020-08-03    0.0
  6. 3   my-group  type-s       NY     DTop 2020-08-04    0.0
  7. 4   my-group  type-s       NY     DTop 2020-08-05   10.0
  8. 5   my-group  type-s       NY     DTop 2020-08-06   10.0
  9. 6   my-group  type-s       NY     DTop 2020-08-07   20.0
  10. 7   my-group  type-s       NY     DTop 2020-08-08   20.0
  11. 8   my-group  type-s       NY     DTop 2020-08-09   20.0
  12. 9   my-group  type-s       NY     DTop 2020-08-10   30.0
  13. 10  my-group  type-s       NY    Tower 2020-08-01   40.0
  14. 11  my-group  type-s       NY    Tower 2020-08-02   40.0
  15. 12  my-group  type-s       NY    Tower 2020-08-03   40.0
  16. 13  my-group  type-s       NY    Tower 2020-08-04   40.0
  17. 14  my-group  type-s       NY    Tower 2020-08-05   40.0
  18. 15  my-group  type-s       NY    Tower 2020-08-06   40.0
  19. 16  my-group  type-s       NY    Tower 2020-08-07   50.0
  20. 17  my-group  type-s       NY    Tower 2020-08-08   50.0
  21. 18  my-group  type-s       NY    Tower 2020-08-09   50.0
  22. 19  my-group  type-s       NY    Tower 2020-08-10   50.0
  23. 20  ot-group  type-t       NY     LTop 2020-08-01    0.0
  24. 21  ot-group  type-t       NY     LTop 2020-08-02    0.0
  25. 22  ot-group  type-t       NY     LTop 2020-08-03    0.0
  26. 23  ot-group  type-t       NY     LTop 2020-08-04    0.0
  27. 24  ot-group  type-t       NY     LTop 2020-08-05    0.0
  28. 25  ot-group  type-t       NY     LTop 2020-08-06    0.0
  29. 26  ot-group  type-t       NY     LTop 2020-08-07    0.0
  30. 27  ot-group  type-t       NY     LTop 2020-08-08   90.0
  31. 28  ot-group  type-t       NY     LTop 2020-08-09   90.0
  32. 29  ot-group  type-t       NY     LTop 2020-08-10   90.0

然而，只使用pivot_table而不使用unstack可能更好：

  1. >>> (df.pivot_table(index='date', columns=cols, values='value')
  2.        .reindex(dti.rename('date')).ffill().fillna(0))
  4. group      my-group       ot-group
  5. rtype        type-s         type-t
  6. location         NY             NY
  7. hardware       DTop Tower     LTop
  8. date                              
  9. 2020-08-01      0.0  40.0      0.0
  10. 2020-08-02      0.0  40.0      0.0
  11. 2020-08-03      0.0  40.0      0.0
  12. 2020-08-04      0.0  40.0      0.0
  13. 2020-08-05     10.0  40.0      0.0
  14. 2020-08-06     10.0  40.0      0.0
  15. 2020-08-07     20.0  50.0      0.0
  16. 2020-08-08     20.0  50.0     90.0
  17. 2020-08-09     20.0  50.0     90.0
  18. 2020-08-10     30.0  50.0     90.0
英文:

You forgot to convert date column as DatetimeIndex:

  1. df = pd.read_csv(&#39;data.csv&#39;, parse_dates=[&#39;date&#39;])
  2. # your code here

Another way with pivot_table:

  1. # Not needed if you used pd.read_csv(..., parse_dates=[&#39;date&#39;])
  2. df[&#39;date&#39;] = pd.to_datetime(df[&#39;date&#39;])
  3. dti = pd.date_range(df[&#39;date&#39;].min(), df[&#39;date&#39;].max(), freq=&#39;D&#39;)
  4. cols = [&#39;group&#39;,&#39;rtype&#39;,&#39;location&#39;,&#39;hardware&#39;]
  5. out = (df.pivot_table(index=&#39;date&#39;, columns=cols, values=&#39;value&#39;)
  6. .reindex(dti.rename(&#39;date&#39;)).ffill().fillna(0)
  7. .unstack().rename(&#39;value&#39;).reset_index())

Output:

  1. &gt;&gt;&gt; out
  2. group   rtype location hardware       date  value
  3. 0   my-group  type-s       NY     DTop 2020-08-01    0.0
  4. 1   my-group  type-s       NY     DTop 2020-08-02    0.0
  5. 2   my-group  type-s       NY     DTop 2020-08-03    0.0
  6. 3   my-group  type-s       NY     DTop 2020-08-04    0.0
  7. 4   my-group  type-s       NY     DTop 2020-08-05   10.0
  8. 5   my-group  type-s       NY     DTop 2020-08-06   10.0
  9. 6   my-group  type-s       NY     DTop 2020-08-07   20.0
  10. 7   my-group  type-s       NY     DTop 2020-08-08   20.0
  11. 8   my-group  type-s       NY     DTop 2020-08-09   20.0
  12. 9   my-group  type-s       NY     DTop 2020-08-10   30.0
  13. 10  my-group  type-s       NY    Tower 2020-08-01   40.0
  14. 11  my-group  type-s       NY    Tower 2020-08-02   40.0
  15. 12  my-group  type-s       NY    Tower 2020-08-03   40.0
  16. 13  my-group  type-s       NY    Tower 2020-08-04   40.0
  17. 14  my-group  type-s       NY    Tower 2020-08-05   40.0
  18. 15  my-group  type-s       NY    Tower 2020-08-06   40.0
  19. 16  my-group  type-s       NY    Tower 2020-08-07   50.0
  20. 17  my-group  type-s       NY    Tower 2020-08-08   50.0
  21. 18  my-group  type-s       NY    Tower 2020-08-09   50.0
  22. 19  my-group  type-s       NY    Tower 2020-08-10   50.0
  23. 20  ot-group  type-t       NY     LTop 2020-08-01    0.0
  24. 21  ot-group  type-t       NY     LTop 2020-08-02    0.0
  25. 22  ot-group  type-t       NY     LTop 2020-08-03    0.0
  26. 23  ot-group  type-t       NY     LTop 2020-08-04    0.0
  27. 24  ot-group  type-t       NY     LTop 2020-08-05    0.0
  28. 25  ot-group  type-t       NY     LTop 2020-08-06    0.0
  29. 26  ot-group  type-t       NY     LTop 2020-08-07    0.0
  30. 27  ot-group  type-t       NY     LTop 2020-08-08   90.0
  31. 28  ot-group  type-t       NY     LTop 2020-08-09   90.0
  32. 29  ot-group  type-t       NY     LTop 2020-08-10   90.0

However, it could be better to use only pivot_table without unstack:

  1. &gt;&gt;&gt; (df.pivot_table(index=&#39;date&#39;, columns=cols, values=&#39;value&#39;)
  2. .reindex(dti.rename(&#39;date&#39;)).ffill().fillna(0))
  3. group      my-group       ot-group
  4. rtype        type-s         type-t
  5. location         NY             NY
  6. hardware       DTop Tower     LTop
  7. date                              
  8. 2020-08-01      0.0  40.0      0.0
  9. 2020-08-02      0.0  40.0      0.0
  10. 2020-08-03      0.0  40.0      0.0
  11. 2020-08-04      0.0  40.0      0.0
  12. 2020-08-05     10.0  40.0      0.0
  13. 2020-08-06     10.0  40.0      0.0
  14. 2020-08-07     20.0  50.0      0.0
  15. 2020-08-08     20.0  50.0     90.0
  16. 2020-08-09     20.0  50.0     90.0
  17. 2020-08-10     30.0  50.0     90.0

答案2

得分: 0

以下是代码部分的翻译：

  1. # pip install pyjanitor
  2. import janitor
  3. import pandas as pd
  5. df = pd.read_clipboard(parse_dates=['date'])
  6. dti = pd.date_range(df['date'].min(), df['date'].max(), freq='D')
  7. cols = ['group', 'rtype', 'location', 'hardware']
  9. (df
  10. .complete(cols, {'date': dti})
  11. .assign(value = lambda f: f.value.ffill().fillna(0, downcast='infer'))
  12. )

请注意，这是代码的翻译，没有其他内容。

英文:

One option is with complete from pyjanitor:

  1. # pip install pyjanitor
  2. import janitor 
  3. import pandas as pd
  5. df = pd.read_clipboard(parse_dates=[&#39;date&#39;])
  6. dti = pd.date_range(df[&#39;date&#39;].min(), df[&#39;date&#39;].max(), freq=&#39;D&#39;)
  7. cols = [&#39;group&#39;,&#39;rtype&#39;,&#39;location&#39;,&#39;hardware&#39;]
  9. (df
  10. .complete(cols, {&#39;date&#39;: dti})
  11. .assign(value = lambda f: f.value.ffill().fillna(0, downcast=&#39;infer&#39;))
  12. )
  14.        group   rtype location hardware       date  value
  15. 0   my-group  type-s       NY     DTop 2020-08-01      0
  16. 1   my-group  type-s       NY     DTop 2020-08-02      0
  17. 2   my-group  type-s       NY     DTop 2020-08-03      0
  18. 3   my-group  type-s       NY     DTop 2020-08-04      0
  19. 4   my-group  type-s       NY     DTop 2020-08-05     10
  20. 5   my-group  type-s       NY     DTop 2020-08-06     10
  21. 6   my-group  type-s       NY     DTop 2020-08-07     20
  22. 7   my-group  type-s       NY     DTop 2020-08-08     20
  23. 8   my-group  type-s       NY     DTop 2020-08-09     20
  24. 9   my-group  type-s       NY     DTop 2020-08-10     30
  25. 10  my-group  type-s       NY    Tower 2020-08-01     40
  26. 11  my-group  type-s       NY    Tower 2020-08-02     40
  27. 12  my-group  type-s       NY    Tower 2020-08-03     40
  28. 13  my-group  type-s       NY    Tower 2020-08-04     40
  29. 14  my-group  type-s       NY    Tower 2020-08-05     40
  30. 15  my-group  type-s       NY    Tower 2020-08-06     40
  31. 16  my-group  type-s       NY    Tower 2020-08-07     50
  32. 17  my-group  type-s       NY    Tower 2020-08-08     50
  33. 18  my-group  type-s       NY    Tower 2020-08-09     50
  34. 19  my-group  type-s       NY    Tower 2020-08-10     50
  35. 20  ot-group  type-t       NY     LTop 2020-08-01     50
  36. 21  ot-group  type-t       NY     LTop 2020-08-02     50
  37. 22  ot-group  type-t       NY     LTop 2020-08-03     50
  38. 23  ot-group  type-t       NY     LTop 2020-08-04     50
  39. 24  ot-group  type-t       NY     LTop 2020-08-05     50
  40. 25  ot-group  type-t       NY     LTop 2020-08-06     50
  41. 26  ot-group  type-t       NY     LTop 2020-08-07     50
  42. 27  ot-group  type-t       NY     LTop 2020-08-08     90
  43. 28  ot-group  type-t       NY     LTop 2020-08-09     90
  44. 29  ot-group  type-t       NY     LTop 2020-08-10     90

@corralien's suggestion of a pivot table without an unstack might be a better one, it all depends on your end goal

huangapple
  • 本文由 发表于 2023年5月25日 12:17:48
  • 转载请务必保留本文链接：https://go.coder-hub.com/76328903.html
  • dataframe
  • pandas
  • python
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