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name

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name Punditsdkoslkdosdkoskdo

Add missing dates to pandas dataframe

My data can have multiple events on a given date or NO events on a date. I take these events, get a count by date and plot them. However, when I plot them, my two series don't always match.

idx = pd.date_range(df['simpleDate'].min(), df['simpleDate'].max())
s = df.groupby(['simpleDate']).size()

In the above code idx becomes a range of say 30 dates. 09-01-2013 to 09-30-2013 However S may only have 25 or 26 days because no events happened for a given date. I then get an AssertionError as the sizes dont match when I try to plot:

fig, ax = plt.subplots()    
ax.bar(idx.to_pydatetime(), s, color='green')

What's the proper way to tackle this? Do I want to remove dates with no values from IDX or (which I'd rather do) is add to the series the missing date with a count of 0. I'd rather have a full graph of 30 days with 0 values. If this approach is right, any suggestions on how to get started? Do I need some sort of dynamic reindex function?

Here's a snippet of S ( df.groupby(['simpleDate']).size() ), notice no entries for 04 and 05.

09-02-2013     2
09-03-2013    10
09-06-2013     5
09-07-2013     1

You could use Series.reindex:

import pandas as pd

idx = pd.date_range('09-01-2013', '09-30-2013')

s = pd.Series({'09-02-2013': 2,
               '09-03-2013': 10,
               '09-06-2013': 5,
               '09-07-2013': 1})
s.index = pd.DatetimeIndex(s.index)

s = s.reindex(idx, fill_value=0)
print(s)

yields

2013-09-01     0
2013-09-02     2
2013-09-03    10
2013-09-04     0
2013-09-05     0
2013-09-06     5
2013-09-07     1
2013-09-08     0
...
  • 252
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A quicker workaround is to use .asfreq(). This doesn't require creation of a new index to call within .reindex().

# "broken" (staggered) dates
dates = pd.Index([pd.Timestamp('2012-05-01'), 
                  pd.Timestamp('2012-05-04'), 
                  pd.Timestamp('2012-05-06')])
s = pd.Series([1, 2, 3], dates)

print(s.asfreq('D'))
2012-05-01    1.0
2012-05-02    NaN
2012-05-03    NaN
2012-05-04    2.0
2012-05-05    NaN
2012-05-06    3.0
Freq: D, dtype: float64
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    • I really prefer this method; you avoid having to call date_range since it implicitly uses the first and last index as the start and end (which is what you would almost always want).

One issue is that reindex will fail if there are duplicate values. Say we're working with timestamped data, which we want to index by date:

df = pd.DataFrame({
    'timestamps': pd.to_datetime(
        ['2016-11-15 1:00','2016-11-16 2:00','2016-11-16 3:00','2016-11-18 4:00']),
    'values':['a','b','c','d']})
df.index = pd.DatetimeIndex(df['timestamps']).floor('D')
df

yields

            timestamps             values
2016-11-15  "2016-11-15 01:00:00"  a
2016-11-16  "2016-11-16 02:00:00"  b
2016-11-16  "2016-11-16 03:00:00"  c
2016-11-18  "2016-11-18 04:00:00"  d

Due to the duplicate 2016-11-16 date, an attempt to reindex:

all_days = pd.date_range(df.index.min(), df.index.max(), freq='D')
df.reindex(all_days)

fails with:

...
ValueError: cannot reindex from a duplicate axis

(by this it means the index has duplicates, not that it is itself a dup)

Instead, we can use .loc to look up entries for all dates in range:

df.loc[all_days]

yields

            timestamps             values
2016-11-15  "2016-11-15 01:00:00"  a
2016-11-16  "2016-11-16 02:00:00"  b
2016-11-16  "2016-11-16 03:00:00"  c
2016-11-17  NaN                    NaN
2016-11-18  "2016-11-18 04:00:00"  d

fillna can be used on the column series to fill blanks if needed.

  • 26
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An alternative approach is resample, which can handle duplicate dates in addition to missing dates. For example:

df.resample('D').mean()

resample is a deferred operation like groupby so you need to follow it with another operation. In this case mean works well, but you can also use many other pandas methods like max, sum, etc.

Here is the original data, but with an extra entry for '2013-09-03':

             val
date           
2013-09-02     2
2013-09-03    10
2013-09-03    20    <- duplicate date added to OP's data
2013-09-06     5
2013-09-07     1

And here are the results:

             val
date            
2013-09-02   2.0
2013-09-03  15.0    <- mean of original values for 2013-09-03
2013-09-04   NaN    <- NaN b/c date not present in orig
2013-09-05   NaN    <- NaN b/c date not present in orig
2013-09-06   5.0
2013-09-07   1.0

I left the missing dates as NaNs to make it clear how this works, but you can add fillna(0) to replace NaNs with zeroes as requested by the OP or alternatively use something like interpolate() to fill with non-zero values based on the neighboring rows.

  • 19
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Here's a nice method to fill in missing dates into a dataframe, with your choice of fill_value, days_back to fill in, and sort order (date_order) by which to sort the dataframe:

def fill_in_missing_dates(df, date_col_name = 'date',date_order = 'asc', fill_value = 0, days_back = 30):

    df.set_index(date_col_name,drop=True,inplace=True)
    df.index = pd.DatetimeIndex(df.index)
    d = datetime.now().date()
    d2 = d - timedelta(days = days_back)
    idx = pd.date_range(d2, d, freq = "D")
    df = df.reindex(idx,fill_value=fill_value)
    df[date_col_name] = pd.DatetimeIndex(df.index)

    return df
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