python - Add missing day rows in stock market data to maintain continuity in pandas dataframe

Question

So I have around 13 years of stock market data of daily low high open close. The problem is the markets are closed sometimes in between and hence Monday to Friday might not appear continuously sometimes. Look below

	Date		Day	Open	High	Low	Close	Adjusted Close
0	17-09-2007		Monday	6898	6977.2	6843	6897.1	6897.100098
1	18-09-2007		Tuesday	6921.15	7078.95	6883.6	7059.65	7059.649902
2	19-09-2007		Wednesday	7111	7419.35	7111	7401.85	7401.850098
3	20-09-2007		Thursday	7404.95	7462.9	7343.6	7390.15	7390.149902
4	21-09-2007		Friday	7378.3	7506.35	7367.15	7464.5	7464.5
5	25-09-2007		Tuesday	7658.5	7694.25	7490.2	7629.15	7629.149902
7	26-09-2007		Wednesday	7647.1	7829.85	7591.8	7755.9	7755.899902
8	28-09-2007		Friday	7838.25	8082.85	7836.05	8042.2	8042.200195
9	01-10-2007		Monday	8008.55	8085.15	7913.3	7987.5	7987.5
10	03-10-2007		Wednesday	8029.8	8235.8	7820.25	8097.9	8097.899902
11	04-10-2007		Thursday	8083.3	8086.7	7828.65	8035.9	8035.899902
12	05-10-2007		Friday	8038.1	8066.55	7789.7	7845.25	7845.25
13	08-10-2007		Monday	7853.15	7935.45	7516.45	7626.4	7626.399902
14	09-10-2007		Tuesday	7580.9	7916.45	7535.05	7895.85	7895.850098
15	10-10-2007		Wednesday	7960.65	8081.05	7907.35	8030.65	8030.649902
16	11-10-2007		Thursday	8054.3	8177.75	8005.5	8158.8	8158.799805
17	15-10-2007		Monday	7962.55	8306.35	7962.55	8286.3	8286.299805
18	16-10-2007		Tuesday	8361.4	8491.65	8240.3	8452.2	8452.200195
19	17-10-2007		Wednesday	8071.55	8218.1	7641.5	8099.9	8099.899902
20	18-10-2007		Thursday	8055.15	8192.45	7519.7	7608.75	7608.75
21	19-10-2007		Friday	7637.4	7718.9	7279.3	7423.8	7423.799805

Answer 1

process:

1. set index of the date
2. resample the dataframe with 'D'
3. drop the day is Sat and Sun
4. fillna with 'ffill'

# Data Index should be timeSeries
df['Date'] = pd.to_datetime(df['Date'], format='%d-%m-%Y')

# upsample
dfn = df.set_index('Date').resample('D').asfreq()

# covert Date to Monday .. Sunday
dfn['Day'] = dfn.index.strftime('%A')

# drop Day in ['Saturday', 'Sunday']
cond = dfn['Day'].isin(['Saturday', 'Sunday'])
dfn = dfn[~cond].fillna(method='ffill').reset_index()

output:

        Date        Day     Open     High      Low    Close  Adjusted Close
0  2007-09-17     Monday  6898.00  6977.20  6843.00  6897.10     6897.100098
1  2007-09-18    Tuesday  6921.15  7078.95  6883.60  7059.65     7059.649902
2  2007-09-19  Wednesday  7111.00  7419.35  7111.00  7401.85     7401.850098
3  2007-09-20   Thursday  7404.95  7462.90  7343.60  7390.15     7390.149902
4  2007-09-21     Friday  7378.30  7506.35  7367.15  7464.50     7464.500000
5  2007-09-24     Monday  7378.30  7506.35  7367.15  7464.50     7464.500000
6  2007-09-25    Tuesday  7658.50  7694.25  7490.20  7629.15     7629.149902
7  2007-09-26  Wednesday  7647.10  7829.85  7591.80  7755.90     7755.899902
8  2007-09-27   Thursday  7647.10  7829.85  7591.80  7755.90     7755.899902
9  2007-09-28     Friday  7838.25  8082.85  7836.05  8042.20     8042.200195
10 2007-10-01     Monday  8008.55  8085.15  7913.30  7987.50     7987.500000
11 2007-10-02    Tuesday  8008.55  8085.15  7913.30  7987.50     7987.500000
12 2007-10-03  Wednesday  8029.80  8235.80  7820.25  8097.90     8097.899902
13 2007-10-04   Thursday  8083.30  8086.70  7828.65  8035.90     8035.899902
14 2007-10-05     Friday  8038.10  8066.55  7789.70  7845.25     7845.250000
15 2007-10-08     Monday  7853.15  7935.45  7516.45  7626.40     7626.399902
16 2007-10-09    Tuesday  7580.90  7916.45  7535.05  7895.85     7895.850098
17 2007-10-10  Wednesday  7960.65  8081.05  7907.35  8030.65     8030.649902
18 2007-10-11   Thursday  8054.30  8177.75  8005.50  8158.80     8158.799805
19 2007-10-12     Friday  8054.30  8177.75  8005.50  8158.80     8158.799805
20 2007-10-15     Monday  7962.55  8306.35  7962.55  8286.30     8286.299805
21 2007-10-16    Tuesday  8361.40  8491.65  8240.30  8452.20     8452.200195
22 2007-10-17  Wednesday  8071.55  8218.10  7641.50  8099.90     8099.899902
23 2007-10-18   Thursday  8055.15  8192.45  7519.70  7608.75     7608.750000
24 2007-10-19     Friday  7637.40  7718.90  7279.30  7423.80     7423.799805