Iterating over column, rows, etc

import pandas as pd
from matplotlib import pyplot as plt

Iterating in python

for a in ['a', 'b', 'c']:
    print(a)

a
b
c

for i, a in enumerate(['a', 'b', 'c']):
    print(i, a)

0 a
1 b
2 c

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

Iterating over column names of a dataframe

for col in df:
    print(col)

Time
USD
JPY
BGN
CZK
DKK
GBP
CHF

for col in df.columns:
    print(col)

Time
USD
JPY
BGN
CZK
DKK
GBP
CHF

Iterating over rows of a dataframe, with each row as serie

for idx, row in df.iterrows():
    print("index is", idx)
    print()
    assert isinstance(row, pd.Series)
    print(row)
    break

index is 2024-01-17 00:00:00

Time    2024-01-17 00:00:00
USD                  1.0877
JPY                  160.65
BGN                  1.9558
CZK                  24.755
DKK                  7.4586
GBP                 0.85818
CHF                  0.9406
Name: 2024-01-17 00:00:00, dtype: object

itertuples: iterating over rows as named tuples

l = [1, 2, 3]
l[0]= 4
l

[4, 2, 3]

t = (1, 2, 3)
t[0] = 4

TypeError: 'tuple' object does not support item assignment

for row in df.itertuples():
    assert isinstance(row, tuple)
    print('index is', row.Index)
    print('USD rate is', row.USD)
    print("JPY rate is", row.JPY)
    break

index is 2024-01-17 00:00:00
USD rate is 1.0877
JPY rate is 160.65

Iterating over columns to get the corresponding series

d = {'c1': "a", 'c2': 'b', 'c3': 'c'}
for k, v in d.items():
    print('key', k, 'value is', d[k])

key c1 value is a
key c2 value is b
key c3 value is c

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

for column_label, column_as_serie in df.items():
    print(column_label)
    print(column_as_serie)
    break

Time
Time
2024-01-17   2024-01-17
2024-01-16   2024-01-16
2024-01-15   2024-01-15
2024-01-12   2024-01-12
2024-01-11   2024-01-11
                ...    
2023-10-26   2023-10-26
2023-10-25   2023-10-25
2023-10-24   2023-10-24
2023-10-23   2023-10-23
2023-10-20   2023-10-20
Name: Time, Length: 61, dtype: datetime64[ns]

type(column_as_serie)

pandas.core.series.Series

Do not modify objects you are iterating over

df = pd.DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})
df

	a	b
0	1	a
1	2	b
2	3	c

for index, row in df.iterrows():
    row["a"] = 10

df

	a	b
0	1	a
1	2	b
2	3	c