Accessing a specific date and got the previous close
Question:
Here is my dataframe:
timestamp open high low close volume
0 2023-01-03 3.5000 3.5972 3.3700 3.500 25231.0
1 2023-01-04 3.5000 3.5500 3.3000 3.505 40815.0
2 2023-01-05 3.5800 3.5800 3.4200 3.450 23833.0
3 2023-01-06 3.4500 3.5400 3.4300 3.500 12298.0
4 2023-01-09 3.5200 4.1500 3.4500 4.150 84826.0
5 2023-01-10 4.1800 4.1800 3.9000 4.010 26032.0
6 2023-01-11 4.1000 4.1000 3.9000 4.000 34807.0
7 2023-01-12 3.9900 4.4200 3.9500 4.190 39737.0
8 2023-01-13 4.4700 4.7500 4.3250 4.540 70672.0
9 2023-01-17 4.8100 5.4000 4.7100 4.780 213792.0
10 2023-01-18 4.6700 4.7475 4.3300 4.380 60063.0
11 2023-01-19 4.3400 4.6100 4.3400 4.420 29150.0
12 2023-01-20 4.3600 4.4400 4.3000 4.300 22088.0
13 2023-01-23 4.2500 4.3242 4.1118 4.150 25304.0
14 2023-01-24 4.2115 4.2499 4.1400 4.170 11240.0
15 2023-01-25 4.3050 4.5000 4.1700 4.220 35726.0
16 2023-01-26 4.2500 4.3000 3.8400 3.980 29257.0
17 2023-01-27 3.8800 4.0200 3.7400 3.770 47719.0
18 2023-01-30 3.7700 3.8700 3.4000 3.480 42704.0
19 2023-01-31 3.4000 3.6599 3.3100 3.570 275723.0
20 2023-02-01 3.4900 3.6500 3.4900 3.600 10763.0
21 2023-02-02 3.7500 3.7800 3.6300 3.750 51914.0
22 2023-02-03 3.7900 3.8899 3.6500 3.790 21763.0
23 2023-02-06 3.9000 4.0600 3.6500 3.700 37767.0
24 2023-02-07 3.7400 3.8500 3.5000 3.660 18919.0
25 2023-02-08 3.6900 3.6900 3.5500 3.610 7878.0
26 2023-02-09 3.6000 3.6000 3.5000 3.560 8594.0
27 2023-02-10 3.5000 3.5500 3.3102 3.420 34729.0
28 2023-02-13 3.4800 3.4800 3.1750 3.300 100681.0
29 2023-02-14 3.2800 3.3400 3.1300 3.180 76240.0
30 2023-02-15 3.2400 3.3000 3.1700 3.290 32600.0
31 2023-02-16 3.4400 3.8800 3.3000 3.880 133655.0
32 2023-02-17 3.9200 4.4300 3.9200 4.350 149117.0
33 2023-02-21 6.5300 7.6100 6.0600 6.400 40559286.0
I can access the row associate with the date 2023-02-21 in using new_df = df.loc[olhc_df["timestamp"] == datetime.date(2023,2,21).strftime("%Y-%m-%d")]
I would like to access the previous close. How can I do it? My idea is to point on the row associate with the date 2023-02-21 and just get the previous close.
I tried using new_df.shift(-1).close, but I obviously got something wrong.
Answers:
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['prev_close'] = df.close.shift(1)
timestamp open high low close volume prev_close
0 2023-01-03 3.5000 3.5972 3.3700 3.500 25231 NaN
1 2023-01-04 3.5000 3.5500 3.3000 3.505 40815 3.500
2 2023-01-05 3.5800 3.5800 3.4200 3.450 23833 3.505
3 2023-01-06 3.4500 3.5400 3.4300 3.500 12298 3.450
4 2023-01-09 3.5200 4.1500 3.4500 4.150 84826 3.500
5 2023-01-10 4.1800 4.1800 3.9000 4.010 26032 4.150
6 2023-01-11 4.1000 4.1000 3.9000 4.000 34807 4.010
7 2023-01-12 3.9900 4.4200 3.9500 4.190 39737 4.000
8 2023-01-13 4.4700 4.7500 4.3250 4.540 70672 4.190
9 2023-01-17 4.8100 5.4000 4.7100 4.780 213792 4.540
10 2023-01-18 4.6700 4.7475 4.3300 4.380 60063 4.780
Here is my dataframe:
timestamp open high low close volume
0 2023-01-03 3.5000 3.5972 3.3700 3.500 25231.0
1 2023-01-04 3.5000 3.5500 3.3000 3.505 40815.0
2 2023-01-05 3.5800 3.5800 3.4200 3.450 23833.0
3 2023-01-06 3.4500 3.5400 3.4300 3.500 12298.0
4 2023-01-09 3.5200 4.1500 3.4500 4.150 84826.0
5 2023-01-10 4.1800 4.1800 3.9000 4.010 26032.0
6 2023-01-11 4.1000 4.1000 3.9000 4.000 34807.0
7 2023-01-12 3.9900 4.4200 3.9500 4.190 39737.0
8 2023-01-13 4.4700 4.7500 4.3250 4.540 70672.0
9 2023-01-17 4.8100 5.4000 4.7100 4.780 213792.0
10 2023-01-18 4.6700 4.7475 4.3300 4.380 60063.0
11 2023-01-19 4.3400 4.6100 4.3400 4.420 29150.0
12 2023-01-20 4.3600 4.4400 4.3000 4.300 22088.0
13 2023-01-23 4.2500 4.3242 4.1118 4.150 25304.0
14 2023-01-24 4.2115 4.2499 4.1400 4.170 11240.0
15 2023-01-25 4.3050 4.5000 4.1700 4.220 35726.0
16 2023-01-26 4.2500 4.3000 3.8400 3.980 29257.0
17 2023-01-27 3.8800 4.0200 3.7400 3.770 47719.0
18 2023-01-30 3.7700 3.8700 3.4000 3.480 42704.0
19 2023-01-31 3.4000 3.6599 3.3100 3.570 275723.0
20 2023-02-01 3.4900 3.6500 3.4900 3.600 10763.0
21 2023-02-02 3.7500 3.7800 3.6300 3.750 51914.0
22 2023-02-03 3.7900 3.8899 3.6500 3.790 21763.0
23 2023-02-06 3.9000 4.0600 3.6500 3.700 37767.0
24 2023-02-07 3.7400 3.8500 3.5000 3.660 18919.0
25 2023-02-08 3.6900 3.6900 3.5500 3.610 7878.0
26 2023-02-09 3.6000 3.6000 3.5000 3.560 8594.0
27 2023-02-10 3.5000 3.5500 3.3102 3.420 34729.0
28 2023-02-13 3.4800 3.4800 3.1750 3.300 100681.0
29 2023-02-14 3.2800 3.3400 3.1300 3.180 76240.0
30 2023-02-15 3.2400 3.3000 3.1700 3.290 32600.0
31 2023-02-16 3.4400 3.8800 3.3000 3.880 133655.0
32 2023-02-17 3.9200 4.4300 3.9200 4.350 149117.0
33 2023-02-21 6.5300 7.6100 6.0600 6.400 40559286.0
I can access the row associate with the date 2023-02-21 in using new_df = df.loc[olhc_df["timestamp"] == datetime.date(2023,2,21).strftime("%Y-%m-%d")]
I would like to access the previous close. How can I do it? My idea is to point on the row associate with the date 2023-02-21 and just get the previous close.
I tried using new_df.shift(-1).close, but I obviously got something wrong.
df['timestamp'] = pd.to_datetime(df['timestamp'])
df['prev_close'] = df.close.shift(1)
timestamp open high low close volume prev_close
0 2023-01-03 3.5000 3.5972 3.3700 3.500 25231 NaN
1 2023-01-04 3.5000 3.5500 3.3000 3.505 40815 3.500
2 2023-01-05 3.5800 3.5800 3.4200 3.450 23833 3.505
3 2023-01-06 3.4500 3.5400 3.4300 3.500 12298 3.450
4 2023-01-09 3.5200 4.1500 3.4500 4.150 84826 3.500
5 2023-01-10 4.1800 4.1800 3.9000 4.010 26032 4.150
6 2023-01-11 4.1000 4.1000 3.9000 4.000 34807 4.010
7 2023-01-12 3.9900 4.4200 3.9500 4.190 39737 4.000
8 2023-01-13 4.4700 4.7500 4.3250 4.540 70672 4.190
9 2023-01-17 4.8100 5.4000 4.7100 4.780 213792 4.540
10 2023-01-18 4.6700 4.7475 4.3300 4.380 60063 4.780