CODEWITHSUNDEEP
pythonpandasdataframe
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Reputation: 33

Pandas DataFrame insert / fill missing rows from previous dates

I have a DataFrame consisting of dates, other columns and a numerical value, where some value combinations in "other columns" could be missing, and I want to populate them from previous dates.

Example. Say the DataFrame is like below. You can see on 2016-01-01, we have data for (LN, A), (LN, B), (NY, A) and (NY, B) on columns (location, band).

        date  location  band  value
0 2016-01-01        LN     A   10.0
1 2016-01-01        LN     B    5.0
2 2016-01-01        NY     A    9.0
3 2016-01-01        NY     B    6.0
4 2016-01-02        LN     A   11.0
5 2016-01-02        NY     B    7.0
6 2016-01-03        NY     A   10.0

Then you notice on 2016-01-02, we only have (LN, A) and (NY, B), but (LN, B) and (NY, A) are missing. Again, on 2016-01-03, only (NY, A) is available; all other three combinations are missing.

What I want to do is to populate the missing combinations of each date from its predecessor. Say for 2016-01-02, I would like to add two more rows, "rolled over" from 2016-01-01: (LN, B, 5.0) and (NY, A, 9.0) for columns (location, band, value). Same for 2016-01-03. So as to make the whole thing like below:

        date  location  band  value
 0 2016-01-01        LN     A   10.0
 1 2016-01-01        LN     B    5.0
 2 2016-01-01        NY     A    9.0
 3 2016-01-01        NY     B    6.0
 4 2016-01-02        LN     A   11.0
 5 2016-01-02        NY     B    7.0
 6 2016-01-03        NY     A   10.0
 7 2016-01-02        LN     B    5.0
 8 2016-01-02        NY     A    9.0
 9 2016-01-03        LN     A   11.0
10 2016-01-03        LN     B    5.0
11 2016-01-03        NY     B    7.0

Note rows 7-11 are populated from rows 1, 2, 4, 7 and 5, respectively. The order is not really important as I can always sort afterwards if all the data I need is present.

Anyone to help? Thanks a lot!

Upvotes: 1

Views: 2519

Answers (2)

Dickster
Dickster

Reputation: 3009

My solution, in summary using the product operation to get all the combinations in a multi index, then some stacking and ffill().

df =pd.DataFrame({'date': {0: '2016-01-01', 1: '2016-01-01', 2: '2016-01-01', 3: '2016-01-01', 4: '2016-01-02', 5: '2016-01-02', 6: '2016-01-03'}, 'band': {0: 'A', 1: 'B', 2: 'A', 3: 'B', 4: 'A', 5: 'B', 6: 'A'}, 'location': {0: 'LN', 1: 'LN', 2: 'NY', 3: 'NY', 4: 'LN', 5: 'NY', 6: 'NY'}, 'value': {0: 10, 1: 5, 2: 9, 3: 6, 4: 11, 5: 7, 6: 10}})
unique_dates = df['date'].unique()
df.set_index(['date','location','band'],inplace=True)
idx = pd.MultiIndex.from_product([unique_dates,['LN','NY'],['A','B']])
df  = df.reindex(idx)
df = df.unstack(level=[2,1])

which produces:

             value                      
                 A      B       A      B
                LN     LN      NY     NY
2016-01-01 10.0000 5.0000  9.0000 6.0000
2016-01-02 11.0000    nan     nan 7.0000
2016-01-03     nan    nan 10.0000    nan

and finally:

df = df.ffill()
df = df.stack().stack()
print df


                  value
2016-01-01 LN A 10.0000
              B  5.0000
           NY A  9.0000
              B  6.0000
2016-01-02 LN A 11.0000
              B  5.0000
           NY A  9.0000
              B  7.0000
2016-01-03 LN A 11.0000
              B  5.0000
           NY A 10.0000
              B  7.0000

Upvotes: 0

root
root

Reputation: 33843

You can use a unstack/stack method to get all missing values, followed by a forward fill:

# Use unstack/stack to add missing locations.
df = df.set_index(['date', 'location', 'band']) \
       .unstack(level=['location', 'band']) \
       .stack(level=['location', 'band'], dropna=False)

# Forward fill NaN values within ['location', 'band'] groups.
df = df.groupby(level=['location', 'band']).ffill().reset_index()

Or you can directly build a MultiIndex containing all combinations:

# Build the full MultiIndex, set the partial MultiIndex, and reindex.
levels = ['date', 'location', 'band']
full_idx = pd.MultiIndex.from_product([df[col].unique() for col in levels], names=levels)
df = df.set_index(levels).reindex(full_idx)

# Forward fill NaN values within ['location', 'band'] groups.
df = df.groupby(level=['location', 'band']).ffill().reset_index()

The resulting output for either method:

         date location band  value
0  2016-01-01       LN    A   10.0
1  2016-01-01       LN    B    5.0
2  2016-01-01       NY    A    9.0
3  2016-01-01       NY    B    6.0
4  2016-01-02       LN    A   11.0
5  2016-01-02       LN    B    5.0
6  2016-01-02       NY    A    9.0
7  2016-01-02       NY    B    7.0
8  2016-01-03       LN    A   11.0
9  2016-01-03       LN    B    5.0
10 2016-01-03       NY    A   10.0
11 2016-01-03       NY    B    7.0

Upvotes: 2

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