Fill in the missing date values ​​in the Pandas Dataframe column

Question

I use Pandas to store stock price data using data frames. There are 2940 rows in the data set. A snapshot is displayed below:

Time series data does not contain values ​​for Saturday and Sunday. Therefore, the missing values ​​must be filled.
Here is the code I wrote, but it does not solve the problem:

import pandas as pd
import numpy as np
import os
os.chdir('C:/Users/Admin/Analytics/stock-prices')

data  = pd.read_csv('stock-data.csv')

# PriceDate Column - Does not contain Saturday and Sunday stock entries
data['PriceDate'] =  pd.to_datetime(data['PriceDate'], format='%m/%d/%Y')
data = data.sort_index(by=['PriceDate'], ascending=[True])


# Starting date is Aug 25 2004
idx = pd.date_range('08-25-2004',periods=2940,freq='D')


data = data.set_index(idx)
data['newdate']=data.index
newdate=data['newdate'].values   # Create a time series column   


data = pd.merge(newdate, data, on='PriceDate', how='outer')

How to fill in the missing values ​​on Saturday and Sunday?

Answer 1

I think you can use resamplewith ffillor bfill, but up set_indexfrom the column PriceDate:

print (data)
   ID  PriceDate  OpenPrice  HighPrice
0   1  6/24/2016          1          2
1   2  6/23/2016          3          4
2   2  6/22/2016          5          6
3   2  6/21/2016          7          8
4   2  6/20/2016          9         10
5   2  6/17/2016         11         12
6   2  6/16/2016         13         14

data['PriceDate'] =  pd.to_datetime(data['PriceDate'], format='%m/%d/%Y')
data = data.sort_values(by=['PriceDate'], ascending=[True])
data.set_index('PriceDate', inplace=True)
print (data)
            ID  OpenPrice  HighPrice
PriceDate                           
2016-06-16   2         13         14
2016-06-17   2         11         12
2016-06-20   2          9         10
2016-06-21   2          7          8
2016-06-22   2          5          6
2016-06-23   2          3          4
2016-06-24   1          1          2

data = data.resample('D').ffill().reset_index()
print (data)
   PriceDate  ID  OpenPrice  HighPrice
0 2016-06-16   2         13         14
1 2016-06-17   2         11         12
2 2016-06-18   2         11         12
3 2016-06-19   2         11         12
4 2016-06-20   2          9         10
5 2016-06-21   2          7          8
6 2016-06-22   2          5          6
7 2016-06-23   2          3          4
8 2016-06-24   1          1          2

---

data = data.resample('D').bfill().reset_index()
print (data)
   PriceDate  ID  OpenPrice  HighPrice
0 2016-06-16   2         13         14
1 2016-06-17   2         11         12
2 2016-06-18   2          9         10
3 2016-06-19   2          9         10
4 2016-06-20   2          9         10
5 2016-06-21   2          7          8
6 2016-06-22   2          5          6
7 2016-06-23   2          3          4
8 2016-06-24   1          1          2