12.3.10.4.4. Getting data in/out

import pandas as pd
import numpy as np


timestamps = pd.Series(
    np.random.randn(1000), index=pd.date_range("1/1/2000", periods=1000)
)
timestamps = timestamps.cumsum()
dataFrame = pd.DataFrame(
    np.random.randn(1000, 4), index=timestamps.index, columns=["A", "B", "C", "D"]
)
dataFrame = dataFrame.cumsum()

dataFrame.to_csv("foo.csv")
pd.read_csv("foo.csv")

	Unnamed: 0	A	B	C	D
0	2000-01-01	-0.791861	-0.558487	0.260862	-0.827079
1	2000-01-02	-0.728893	0.018522	0.491820	0.427598
2	2000-01-03	0.801129	-0.788797	0.187535	0.424554
3	2000-01-04	0.003962	-1.998664	-0.078471	1.017862
4	2000-01-05	0.529178	-1.765030	-0.781073	1.237441
...	...	...	...	...	...
995	2002-09-22	22.183364	-29.935107	-32.450743	19.144473
996	2002-09-23	21.568148	-30.342366	-32.678239	19.158621
997	2002-09-24	22.086591	-28.859693	-32.059223	18.354344
998	2002-09-25	22.520814	-29.239886	-30.950673	18.254176
999	2002-09-26	24.098983	-29.360780	-29.249539	18.458504

1000 rows × 5 columns

dataFrame.to_excel("foo.xlsx", sheet_name="Sheet1")
pd.read_excel("foo.xlsx", "Sheet1", index_col=None, na_values=["NA"])

	Unnamed: 0	A	B	C	D
0	2000-01-01	-0.791861	-0.558487	0.260862	-0.827079
1	2000-01-02	-0.728893	0.018522	0.491820	0.427598
2	2000-01-03	0.801129	-0.788797	0.187535	0.424554
3	2000-01-04	0.003962	-1.998664	-0.078471	1.017862
4	2000-01-05	0.529178	-1.765030	-0.781073	1.237441
...	...	...	...	...	...
995	2002-09-22	22.183364	-29.935107	-32.450743	19.144473
996	2002-09-23	21.568148	-30.342366	-32.678239	19.158621
997	2002-09-24	22.086591	-28.859693	-32.059223	18.354344
998	2002-09-25	22.520814	-29.239886	-30.950673	18.254176
999	2002-09-26	24.098983	-29.360780	-29.249539	18.458504

1000 rows × 5 columns