Date Range
Read data
import pandas as pd
df = pd.read_csv("apple_stock_price.csv",parse_dates=["Date"], index_col="Date")
df.head(2)
Out[15]:
Open High Low Close Adj Close Volume
Date
2018-07-23 190.679993 191.960007 189.559998 191.610001 188.737030 15989400
2018-07-24 192.449997 193.660004 192.050003 193.000000 190.106216 18697900What is DatetimeIndex? Benefits of it
Partial Date Index: Select Specific Months Data
df["2019-07"]
Out[21]:
Open High Low Close Adj Close Volume
Date
2019-07-01 203.169998 204.490005 200.649994 201.550003 201.550003 27316700
2019-07-02 201.410004 203.130005 201.360001 202.729996 202.729996 16935200
2019-07-03 203.279999 204.440002 202.690002 204.410004 204.410004 11362000
2019-07-05 203.350006 205.080002 202.899994 204.229996 204.229996 17265500
2019-07-08 200.809998 201.399994 198.410004 200.020004 200.020004 25338600
2019-07-09 199.199997 201.509995 198.809998 201.240005 201.240005 20578000
2019-07-10 201.850006 203.729996 201.559998 203.229996 203.229996 17897100
2019-07-11 203.309998 204.389999 201.710007 201.750000 201.750000 20191800
2019-07-12 202.449997 204.000000 202.199997 203.300003 203.300003 17595200
2019-07-15 204.089996 205.869995 204.000000 205.210007 205.210007 16947400
2019-07-16 204.589996 206.110001 203.500000 204.500000 204.500000 16866800
2019-07-17 204.050003 205.089996 203.270004 203.350006 203.350006 14107500
2019-07-18 204.000000 205.880005 203.699997 205.660004 205.660004 18582200
2019-07-19 205.789993 206.500000 202.360001 202.589996 202.589996 20929300
2019-07-22 203.649994 207.229996 203.610001 207.220001 207.220001 22241300
2019-07-23 208.460007 208.789993 207.440002 208.126907 208.126907 5728710df['2019-06'].Close.mean()
Out[26]:
192.96900015df['2019'].head(2)
Out[27]:
Open High Low Close Adj Close Volume
Date
2019-01-02 154.889999 158.850006 154.229996 157.919998 156.642365 37039700
2019-01-03 143.979996 145.720001 142.000000 142.190002 141.039642 91312200Select Date Range
df['2018-12-08':'2019-01-08']
Out[29]:
Open High Low Close Adj Close Volume
Date
2018-12-10 165.000000 170.089996 163.330002 169.600006 168.227890 62026000
2018-12-11 171.660004 171.789993 167.000000 168.630005 167.265732 47281700
2018-12-12 170.399994 171.919998 169.020004 169.100006 167.731934 35627700
2018-12-13 170.490005 172.570007 169.550003 170.949997 169.566956 31898600
2018-12-14 169.000000 169.080002 165.279999 165.479996 164.141220 40703700
2018-12-17 165.449997 168.350006 162.729996 163.940002 162.613678 44287900
2018-12-18 165.380005 167.529999 164.389999 166.070007 164.726440 33841500
2018-12-19 166.000000 167.449997 159.089996 160.889999 159.588348 49047300
2018-12-20 160.399994 162.110001 155.300003 156.830002 155.561188 64773000
2018-12-21 156.860001 158.160004 149.630005 150.729996 149.510544 95744600
2018-12-24 148.149994 151.550003 146.589996 146.830002 145.642090 37169200
2018-12-26 148.300003 157.229996 146.720001 157.169998 155.898438 58582500
2018-12-27 155.839996 156.770004 150.070007 156.149994 154.886688 53117100
2018-12-28 157.500000 158.520004 154.550003 156.229996 154.966034 42291400
2018-12-31 158.529999 159.360001 156.479996 157.740005 156.463837 35003500
2019-01-02 154.889999 158.850006 154.229996 157.919998 156.642365 37039700
2019-01-03 143.979996 145.720001 142.000000 142.190002 141.039642 91312200
2019-01-04 144.529999 148.550003 143.800003 148.259995 147.060516 58607100
2019-01-07 148.699997 148.830002 145.899994 147.929993 146.733185 54777800
2019-01-08 149.559998 151.820007 148.520004 150.750000 149.530380 41025300Resampling
df['Close'].resample('M').mean().head()
Out[30]:
Date
2018-07-31 192.117144
2018-08-31 213.346089
2018-09-30 222.073685
2018-10-31 220.845652
2018-11-30 191.235714
Freq: M, Name: Close, dtype: float64Draw plot with matplotlib
%matplotlib inline
df['Close'].plot()
Out[36]:
<matplotlib.axes._subplots.AxesSubplot at 0x1f0b45b7da0>
# By month
df['Close'].resample('M').mean().plot()Last updated
