pandas Series (1d)

import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import seaborn as sns

pd.Series(["a", "b", "c", "d"])

0    a
1    b
2    c
3    d
dtype: object

a1d = np.arange(17)+0.1
a1d.shape

(17,)

s = pd.Series(a1d)
s

0      0.1
1      1.1
2      2.1
3      3.1
4      4.1
5      5.1
6      6.1
7      7.1
8      8.1
9      9.1
10    10.1
11    11.1
12    12.1
13    13.1
14    14.1
15    15.1
16    16.1
dtype: float64

s.index

RangeIndex(start=0, stop=17, step=1)

Series of dtype

df = pd.read_csv('rates.csv')
df.head()
df.dtypes

Time     object
USD     float64
JPY     float64
BGN     float64
CZK     float64
DKK     float64
GBP     float64
CHF     float64
dtype: object

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

Time    datetime64[ns]
USD            float64
JPY            float64
BGN            float64
CZK            float64
DKK            float64
GBP            float64
CHF            float64
dtype: object

type(df.dtypes)

pandas.core.series.Series

if isinstance(df.dtypes, pd.Series):
    raise ValueError('this is a Series but I was expecting something else')

ValueError: this is a Series but I was expecting something else

df.dtypes.index

Index(['Time', 'USD', 'JPY', 'BGN', 'CZK', 'DKK', 'GBP', 'CHF'], dtype='object')

isinstance(df.dtypes, pd.Series)

True

isinstance(df.columns, pd.Series)

False

isinstance(df.columns, pd.Index)

True

df

	Time	USD	JPY	BGN	CZK	DKK	GBP	CHF
0	2024-01-17	1.0877	160.65	1.9558	24.755	7.4586	0.85818	0.9406
1	2024-01-16	1.0882	159.64	1.9558	24.710	7.4582	0.86078	0.9361
2	2024-01-15	1.0945	159.67	1.9558	24.714	7.4590	0.86075	0.9351
3	2024-01-12	1.0942	159.17	1.9558	24.689	7.4565	0.85950	0.9350
4	2024-01-11	1.0987	159.71	1.9558	24.659	7.4568	0.86145	0.9338
...	...	...	...	...	...	...	...	...
56	2023-10-26	1.0540	158.48	1.9558	24.714	7.4632	0.87170	0.9466
57	2023-10-25	1.0576	158.55	1.9558	24.693	7.4639	0.87240	0.9474
58	2023-10-24	1.0632	159.26	1.9558	24.659	7.4648	0.87025	0.9501
59	2023-10-23	1.0597	158.91	1.9558	24.645	7.4634	0.87153	0.9461
60	2023-10-20	1.0591	158.80	1.9558	24.704	7.4620	0.87213	0.9442

61 rows × 8 columns

assert isinstance(df['USD'], pd.Series)
df['USD']

0     1.0877
1     1.0882
2     1.0945
3     1.0942
4     1.0987
       ...  
56    1.0540
57    1.0576
58    1.0632
59    1.0597
60    1.0591
Name: USD, Length: 61, dtype: float64

isinstance(df['USD'], pd.Series)

True

assert isinstance(df.USD, pd.Series)
df.USD

0     1.0877
1     1.0882
2     1.0945
3     1.0942
4     1.0987
       ...  
56    1.0540
57    1.0576
58    1.0632
59    1.0597
60    1.0591
Name: USD, Length: 61, dtype: float64

df['USD'] # here I extract the USD column, but I lose information about the dates

0     1.0877
1     1.0882
2     1.0945
3     1.0942
4     1.0987
       ...  
56    1.0540
57    1.0576
58    1.0632
59    1.0597
60    1.0591
Name: USD, Length: 61, dtype: float64

# here is how to fix it and use time is index
df.index = df['Time']
df

	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
...	...	...	...	...	...	...	...	...
2023-10-26	2023-10-26	1.0540	158.48	1.9558	24.714	7.4632	0.87170	0.9466
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

61 rows × 8 columns

# Now when we extract a column, we keep the index
# we keep information about the dates
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