head tail empty describe

import numpy as np
import pandas as pd

DataFrame from historal rates data

df = pd.read_csv('rates.csv', parse_dates=['Time'])
df.index = df['Time']
df.head()

	Time	USD	JPY	BGN	CZK	DKK	GBP	CHF
Time
2024-01-17	2024-01-17	1.0877	160.65	1.9558	24.755	7.4586	0.85818	0.9406
2024-01-16	2024-01-16	1.0882	159.64	1.9558	24.710	7.4582	0.86078	0.9361
2024-01-15	2024-01-15	1.0945	159.67	1.9558	24.714	7.4590	0.86075	0.9351
2024-01-12	2024-01-12	1.0942	159.17	1.9558	24.689	7.4565	0.85950	0.9350
2024-01-11	2024-01-11	1.0987	159.71	1.9558	24.659	7.4568	0.86145	0.9338

df.tail(2)

	Time	USD	JPY	BGN	CZK	DKK	GBP	CHF
Time
2023-10-23	2023-10-23	1.0597	158.91	1.9558	24.645	7.4634	0.87153	0.9461
2023-10-20	2023-10-20	1.0591	158.80	1.9558	24.704	7.4620	0.87213	0.9442

# Accessing single columns as attributes or using square brackets
df.USD

Time
2024-01-17    1.0877
2024-01-16    1.0882
2024-01-15    1.0945
2024-01-12    1.0942
2024-01-11    1.0987
               ...  
2023-10-26    1.0540
2023-10-25    1.0576
2023-10-24    1.0632
2023-10-23    1.0597
2023-10-20    1.0591
Name: USD, Length: 61, dtype: float64

# Subsets of columns
df[['USD', 'CHF']]

	USD	CHF
Time
2024-01-17	1.0877	0.9406
2024-01-16	1.0882	0.9361
2024-01-15	1.0945	0.9351
2024-01-12	1.0942	0.9350
2024-01-11	1.0987	0.9338
...	...	...
2023-10-26	1.0540	0.9466
2023-10-25	1.0576	0.9474
2023-10-24	1.0632	0.9501
2023-10-23	1.0597	0.9461
2023-10-20	1.0591	0.9442

61 rows × 2 columns

df[['USD', 'CHF']].head()

	USD	CHF
Time
2024-01-17	1.0877	0.9406
2024-01-16	1.0882	0.9361
2024-01-15	1.0945	0.9351
2024-01-12	1.0942	0.9350
2024-01-11	1.0987	0.9338

df[['USD', 'CHF']].head(3)

	USD	CHF
Time
2024-01-17	1.0877	0.9406
2024-01-16	1.0882	0.9361
2024-01-15	1.0945	0.9351

df[['USD', 'CHF']].tail(4)

	USD	CHF
Time
2023-10-25	1.0576	0.9474
2023-10-24	1.0632	0.9501
2023-10-23	1.0597	0.9461
2023-10-20	1.0591	0.9442

df.tail(4)

	Time	USD	JPY	BGN	CZK	DKK	GBP	CHF
Time
2023-10-25	2023-10-25	1.0576	158.55	1.9558	24.693	7.4639	0.87240	0.9474
2023-10-24	2023-10-24	1.0632	159.26	1.9558	24.659	7.4648	0.87025	0.9501
2023-10-23	2023-10-23	1.0597	158.91	1.9558	24.645	7.4634	0.87153	0.9461
2023-10-20	2023-10-20	1.0591	158.80	1.9558	24.704	7.4620	0.87213	0.9442

df.empty

False

empty_df = pd.DataFrame([])
empty_df.empty

True

# DataFrame with column names can be empty
empty_df2 = pd.DataFrame([], columns=['colA', 'colB', 'colC'])
empty_df2

	colA	colB	colC

empty_df2.empty

True

df.describe()

	Time	USD	JPY	BGN	CZK	DKK	GBP	CHF
count	61	61.000000	61.000000	6.100000e+01	61.000000	61.000000	61.000000	61.000000
mean	2023-12-02 17:42:17.704918016	1.083367	159.357377	1.955800e+00	24.536525	7.458064	0.866149	0.949808
min	2023-10-20 00:00:00	1.053700	154.800000	1.955800e+00	24.260000	7.452900	0.855800	0.926000
25%	2023-11-10 00:00:00	1.070200	157.630000	1.955800e+00	24.420000	7.455900	0.860780	0.942900
50%	2023-12-01 00:00:00	1.087700	159.170000	1.955800e+00	24.543000	7.456900	0.866710	0.947800
75%	2023-12-22 00:00:00	1.094600	161.190000	1.955800e+00	24.659000	7.459000	0.871530	0.962800
max	2024-01-17 00:00:00	1.111400	164.050000	1.955800e+00	24.755000	7.464800	0.876300	0.967300
std	NaN	0.014814	2.371918	2.238873e-16	0.131402	0.003083	0.005824	0.012294