replicate dataframe rows based on a column's value-CodePudding

I have a dataframe of 2 months of data with 20 columns, one of which is 'date' . There are 3 non-consecutive dates on which there is no data. I want to replicate previous day's data to create entries for those missing days as well.

Here is what I tried:

df_replicate=df[(df['date']=='2021-07-27') | (df['date']=='2021-08-18') | (df['date']=='2021-08-22')]

df_replicate.loc[df_replicate['date']=='2021-07-27']='2021-07-28'
df_replicate.loc[df_replicate['date']=='2021-08-18']='2021-08-19'
df_replicate.loc[df_replicate['date']=='2021-08-22']='2021-08-23'

And then concatenate df and df_replicate

What is an easier way to do this?

CodePudding user response：

You could use reindex with the "ffill" parameter:

import pandas as pd
import numpy as np


date_index = pd.date_range('2021-07-27', periods=7, freq='D')

# set data to date time index
df = pd.DataFrame({"prices": [100, np.nan, 100, 89, 88, np.nan, np.nan ]},index=date_index)

2021-07-27  100.0
2021-07-28    NaN
2021-07-29  100.0
2021-07-30   89.0
2021-07-31   88.0
2021-08-01    NaN
2021-08-02    NaN

# remove one of the date values to represent missing data
df = df[~(df.index=='2021-07-28')]

2021-07-27  100.0
2021-07-29  100.0
2021-07-30   89.0
2021-07-31   88.0
2021-08-01    NaN
2021-08-02    NaN

# Second date index with correct number of days
date_index2 = pd.date_range('2021-07-27', periods=7, freq='D')

# df with missing row foward filled
df.reindex(date_index2, method="ffill")

2021-07-27  100.0 #This value is carried to the next date
2021-07-28  100.0 
2021-07-29  100.0
2021-07-30   89.0
2021-07-31   88.0
2021-08-01    NaN
2021-08-02    NaN

https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.reindex.html#pandas.DataFrame.reindex

CodePudding user response：

Filter matched rows by list with next values created by Index.shift and for these pairs use forward filling missing values:

df = pd.DataFrame({"prices": [100, np.nan, 100, 89, 88, np.nan, np.nan ],
                  'date': pd.date_range('2021-07-27', periods=7, freq='D')})


df['date'] = pd.to_datetime(df['date'])

dates = pd.to_datetime(['2021-07-27','2021-08-18','2021-08-22'])

mask = df['date'].isin(dates.append(dates.shift(freq='d')))
df[mask] = df[mask].ffill()
print (df)
   prices       date
0   100.0 2021-07-27
1   100.0 2021-07-28
2   100.0 2021-07-29
3    89.0 2021-07-30
4    88.0 2021-07-31
5     NaN 2021-08-01
6     NaN 2021-08-02

If need replace only next rows (filled by NaNs) by all previous non NaNs:

df['date'] = pd.to_datetime(df['date'])

dates = pd.to_datetime(['2021-07-27','2021-08-18','2021-08-22'])

mask = df['date'].isin(dates.shift(freq='d'))
df[mask] = df.ffill()

If input list is different, next values (['2021-07-28','2021-08-19','2021-08-23']) is necessary shifting for previous matched values:

df['date'] = pd.to_datetime(df['date'])
dates = pd.to_datetime(['2021-07-28','2021-08-19','2021-08-23'])

mask = df['date'].isin(dates.append(dates.shift(-1, freq='d')))
df[mask] = df[mask].ffill()