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Create new Row in Data Frame with ID and date if ID and date do not exist in "x" timeframe [duplicate]

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
...

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

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.

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.

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

You can always just use DataFrame.merge() utilizing a left join from an 'All Dates' DataFrame to the 'Missing Dates' DataFrame. Example below.
# example DataFrame with missing dates between min(date) and max(date)
missing_df = pd.DataFrame({
'date':pd.to_datetime([
'2022-02-10'
,'2022-02-11'
,'2022-02-14'
,'2022-02-14'
,'2022-02-24'
,'2022-02-16'
])
,'value':[10,20,5,10,15,30]
})
# first create a DataFrame with all dates between specified start<-->end using pd.date_range()
all_dates = pd.DataFrame(pd.date_range(missing_df['date'].min(), missing_df['date'].max()), columns=['date'])
# from the all_dates DataFrame, left join onto the DataFrame with missing dates
new_df = all_dates.merge(right=missing_df, how='left', on='date')

s.asfreq('D').interpolate().asfreq('Q')

Pandas resample MultiIndex dataframe with forward fill

I am trying to resample a MultiIndex dataframe to a less granular frequency (daily to month end) by taking the last valid daily observation in every month.
For example, given the dataframe below:
df = pd.DataFrame({'date': [pd.to_datetime('2012-03-29')]*4
+ [pd.to_datetime('2012-03-30')]*4
+ [pd.to_datetime('2012-04-01')]*4,
'groups':[1,2,3,4]*3,
'values':np.random.normal(size=12)})
df = df.set_index(['date', 'groups'])
values
date groups
2012-03-29 1 0.013681
2 0.359522
3 -0.525454
4 -0.282541
2012-03-30 1 0.155501
2 -1.053596
3 0.003049
4 -0.165875
2012-04-01 1 -0.049135
2 2.701785
3 2.240875
4 0.057297
The desired final dataframe is:
values
date groups
2012-03-31 1 0.155501
2 -1.053596
3 0.003049
4 -0.165875
In a regular dataframe (with single index), the desired output can be achieved with df.asfreq('M', method='ffill') as shown below.
df = pd.DataFrame({'date': [pd.to_datetime('2012-03-29')] + pd.date_range('2012-04-01', '2012-04-04').to_list(),
'values':np.random.normal(size=5)})
df = df.set_index('date')
df_monthly = df.asfreq('M', method='ffill')
Where df is:
values
date
2012-03-29 1.988554
2012-04-01 -1.054163
2012-04-02 -1.112537
2012-04-03 0.224515
2012-04-04 0.152175
and df_monthly is:
values
date
2012-03-31 1.988554
Any help is much appreciated. Thanks in advance.

Use:
df_monthly = df.reset_index(level=1).groupby('groups')[['values']].apply(lambda x: x.asfreq('M', method='ffill')).swaplevel(1,0)
print (df_monthly)
values
date groups
2012-03-31 1 -2.951662
2 -1.495653
3 -0.948413
4 0.066219

How to fill missing dates with corresponding NaN in other columns

I have a CSV that initially creates following dataframe:
Date Portfoliovalue
0 2021-05-01 50000.0
1 2021-05-05 52304.0
Using the following script, I would like to fill the missing dates and have a corresponding NaN value in the Portfoliovalue column with NaN. So the result would be this:
Date Portfoliovalue
0 2021-05-01 50000.0
1 2021-05-02 NaN
2 2021-05-03 NaN
3 2021-05-04 NaN
4 2021-05-05 52304.0
I first tried the method here: Fill the missing date values in a Pandas Dataframe column
However the bfill replaces all my NaN's and removing it only returns an error.
So far I have tried this:
df = pd.read_csv("Tickers_test5.csv")
df2 = pd.read_csv("Portfoliovalues.csv")
portfolio_value = df['Currentvalue'].sum()
portfolio_value = portfolio_value + cash
date = datetime.date(datetime.now())
df2.loc[len(df2)] = [date, portfolio_value]
print(df2.asfreq('D'))
However, this only returns this:
Date Portfoliovalue
1970-01-01 NaN NaN
Thanks for your help. I am really impressed at how helpful this community is.
Quick update:
I have added the code, so that it fills my missing dates. However, it is part of a programme, which tries to update the missing dates every time it launches. So when I execute the code and no dates are missing, I get the following error:
ValueError: cannot reindex from a duplicate axis”
The code is as follows:
df2 = pd.read_csv("Portfoliovalues.csv")
portfolio_value = df['Currentvalue'].sum()
date = datetime.date(datetime.now())
df2.loc[date, 'Portfoliovalue'] = portfolio_value
#Solution provided by Uts after asking on Stackoverflow
df2.Date = pd.to_datetime(df2.Date)
df2 = df2.set_index('Date').asfreq('D').reset_index()
So by the looks of it the code adds a duplicate date, which then causes the .reindex() function to raise the ValueError. However, I am not sure how to proceed. Is there an alternative to .reindex() or maybe the assignment of today's date needs changing?

Pandas has asfreq function for datetimeIndex, this is basically just a thin, but convenient wrapper around reindex() which generates a date_range and calls reindex.
Code
df.Date = pd.to_datetime(df.Date)
df = df.set_index('Date').asfreq('D').reset_index()
Output
Date Portfoliovalue
0 2021-05-01 50000.0
1 2021-05-02 NaN
2 2021-05-03 NaN
3 2021-05-04 NaN
4 2021-05-05 52304.0

Pandas has reindex method: given a list of indices, it remains only indices from list.
In your case, you can create all the dates you want, by date_range for example, and then give it to reindex. you might needed a simple set_index and reset_index, but I assume you don't care much about the original index.
Example:
df.set_index('Date').reindex(pd.date_range(start=df['Date'].min(), end=df['Date'].max(), freq='D')).reset_index()
On first we set 'Date' column as index. Then we use reindex, it full list of dates (given by date_range from minimal date to maximal date in 'Date' column, with daily frequency) as new index. It result nans in places without former value.

Boxplot of Multiindex df

I want to do 2 things:
I want to create one boxplot per date/day with all the values for MeanTravelTimeSeconds in that date. The number of MeanTravelTimeSeconds elements varies from date to date (e.g. one day might have a count of 300 values while another, 400).
Also, I want to transform the rows in my multiindex series into columns because I don't want the rows to repeat every time. If it stays like this I'd have tens of millions of unnecessary rows.
Here is the resulting series after using df.stack() on a df indexed by date (date is a datetime object index):
Date
2016-01-02 NumericIndex 1611664
OriginMovementID 4744
DestinationMovementID 5084
MeanTravelTimeSeconds 1233
RangeLowerBoundTravelTimeSeconds 756
...
2020-03-31 DestinationMovementID 3594
MeanTravelTimeSeconds 1778
RangeLowerBoundTravelTimeSeconds 1601
RangeUpperBoundTravelTimeSeconds 1973
DayOfWeek Tuesday
Length: 11281655, dtype: object
When I use seaborn to plot the boxplot I guet a bucnh of errors after playing with different selections.
If I try to do df.stack().unstack() or df.stack().T I get then following error:
Index contains duplicate entries, cannot reshape
How do I plot the boxplot and how do I turn the rows into columns?

You really do need to make your index unique to make the functions you want to work. I suggest a sequential number that resets at every change in the other two key columns.
import datetime as dt
import random
import numpy as np
cat = ["NumericIndex","OriginMovementID","DestinationMovementID","MeanTravelTimeSeconds",
"RangeLowerBoundTravelTimeSeconds"]
df = pd.DataFrame(
[{"Date":d, "Observation":cat[random.randint(0,len(cat)-1)],
"Value":random.randint(1000,10000)}
for i in range(random.randint(5,20))
for d in pd.date_range(dt.datetime(2016,1,2), dt.datetime(2016,3,31), freq="14D")])
# starting point....
df = df.sort_values(["Date","Observation"]).set_index(["Date","Observation"])
# generate an array that is sequential within change of key
seq = np.full(df.index.shape, 0)
s=0
p=""
for i, v in enumerate(df.index):
if i==0 or p!=v: s=0
else: s+=1
seq[i] = s
p=v
df["SeqNo"] = seq
# add to index - now unstack works as required
dfdd = df.set_index(["SeqNo"], append=True)
dfdd.unstack(0).loc["MeanTravelTimeSeconds"].boxplot()
print(dfdd.unstack(1).head().to_string())
output
Value
Observation DestinationMovementID MeanTravelTimeSeconds NumericIndex OriginMovementID RangeLowerBoundTravelTimeSeconds
Date SeqNo
2016-01-02 0 NaN NaN 2560.0 5324.0 5085.0
1 NaN NaN 1066.0 7372.0 NaN
2016-01-16 0 NaN 6226.0 NaN 7832.0 NaN
1 NaN 1384.0 NaN 8839.0 NaN
2 NaN 7892.0 NaN NaN NaN

Pandas reindex and interpolate time series efficiently (reindex drops data)

Suppose I wish to re-index, with linear interpolation, a time series to a pre-defined index, where none of the index values are shared between old and new index. For example
# index is all precise timestamps e.g. 2018-10-08 05:23:07
series = pandas.Series(data,index)
# I want rounded date-times
desired_index = pandas.date_range("2010-10-08",periods=10,freq="30min")
Tutorials/API suggest the way to do this is to reindex then fill NaN values using interpolate. But, as there is no overlap of datetimes between the old and new index, reindex outputs all NaN:
# The following outputs all NaN as no date times match old to new index
series.reindex(desired_index)
I do not want to fill nearest values during reindex as that will lose precision, so I came up with the following; concatenate the reindexed series with the original before interpolating:
pandas.concat([series,series.reindex(desired_index)]).sort_index().interpolate(method="linear")
This seems very inefficient, concatenating and then sorting the two series. Is there a better way?

The only (simple) way I can see of doing this is to use resample to upsample to your time resolution (say 1 second), then reindex.
Get an example DataFrame:
import numpy as np
import pandas as pd
np.random.seed(2)
df = (pd.DataFrame()
.assign(SampleTime=pd.date_range(start='2018-10-01', end='2018-10-08', freq='30T')
+ pd.to_timedelta(np.random.randint(-5, 5, size=337), unit='s'),
Value=np.random.randn(337)
)
.set_index(['SampleTime'])
)
Let's see what the data looks like:
df.head()
Value
SampleTime
2018-10-01 00:00:03 0.033171
2018-10-01 00:30:03 0.481966
2018-10-01 01:00:01 -0.495496
Get the desired index:
desired_index = pd.date_range('2018-10-01', periods=10, freq='30T')
Now, reindex the data with the union of the desired and existing indices, interpolate based on the time, and reindex again using only the desired index:
(df
.reindex(df.index.union(desired_index))
.interpolate(method='time')
.reindex(desired_index)
)
Value
2018-10-01 00:00:00 NaN
2018-10-01 00:30:00 0.481218
2018-10-01 01:00:00 -0.494952
2018-10-01 01:30:00 -0.103270
As you can see, you still have an issue with the first timestamp because it's outside the range of the original index; there are number of ways to deal with this (pad, for example).

my methods
frequency = nyse_trading_dates.rename_axis([None]).index
df = prices.rename_axis([None]).reindex(frequency)
for d in prices.rename_axis([None]).index:
df.loc[d] = prices.loc[d]
df.interpolate(method='linear')
method 2
prices = data.loc[~data.index.duplicated(keep='last')]
#prices = data.reset_index()
idx1 = prices.index
idx1 = pd.to_datetime(idx1, errors='coerce')
merged = idx1.union(idx2)
s = prices.reindex(merged)
df = s.interpolate(method='linear').dropna(axis=0, how='any')
data=df