How about an old school inplace-for-loop for Pandas DataFrames? :)
Project description
Tired of vectorization, df.apply and df.map? How about an old school inplace-for-loop? :)
Usage - DataFrame
pip install
from a_pandas_ex_old_school_for_loop import pd_add_old_school_for_loop
pd_add_old_school_for_loop()
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
df
Out[3]:
PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 3 ... 7.2500 NaN S
1 2 1 1 ... 71.2833 C85 C
2 3 1 3 ... 7.9250 NaN S
3 4 1 1 ... 53.1000 C123 S
4 5 0 3 ... 8.0500 NaN S
.. ... ... ... ... ... ... ...
886 887 0 2 ... 13.0000 NaN S
887 888 1 1 ... 30.0000 B42 S
888 889 0 3 ... 23.4500 NaN S
889 890 1 1 ... 30.0000 C148 C
890 891 0 3 ... 7.7500 NaN Q
[891 rows x 12 columns]
for item in df.ds_set_values_with_loop():
try:
if item['get_value']() > 2:
item['set_value'](10000000)
except Exception:
continue
df
Out[5]:
PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 10000000 ... 10000000.0 NaN S
1 2 1 1 ... 10000000.0 C85 C
2 10000000 1 10000000 ... 10000000.0 NaN S
3 10000000 1 1 ... 10000000.0 C123 S
4 10000000 0 10000000 ... 10000000.0 NaN S
.. ... ... ... ... ... ... ...
886 10000000 0 2 ... 10000000.0 NaN S
887 10000000 1 1 ... 10000000.0 B42 S
888 10000000 0 10000000 ... 10000000.0 NaN S
889 10000000 1 1 ... 10000000.0 C148 C
890 10000000 0 10000000 ... 10000000.0 NaN Q
[891 rows x 12 columns]
Usage - Series in a DataFrame
from a_pandas_ex_old_school_for_loop import pd_add_old_school_for_loop
pd_add_old_school_for_loop()
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
df
Out[3]:
PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 3 ... 7.2500 NaN S
1 2 1 1 ... 71.2833 C85 C
2 3 1 3 ... 7.9250 NaN S
3 4 1 1 ... 53.1000 C123 S
4 5 0 3 ... 8.0500 NaN S
.. ... ... ... ... ... ... ...
886 887 0 2 ... 13.0000 NaN S
887 888 1 1 ... 30.0000 B42 S
888 889 0 3 ... 23.4500 NaN S
889 890 1 1 ... 30.0000 C148 C
890 891 0 3 ... 7.7500 NaN Q
[891 rows x 12 columns]
for item in df.PassengerId.ds_set_values_with_loop():
try:
if item['get_value']() > 2:
item['set_value'](10000000)
except Exception:
continue
df
Out[3]:
PassengerId Survived Pclass ... Fare Cabin Embarked
0 1 0 3 ... 7.2500 NaN S
1 2 1 1 ... 71.2833 C85 C
2 10000000 1 3 ... 7.9250 NaN S
3 10000000 1 1 ... 53.1000 C123 S
4 10000000 0 3 ... 8.0500 NaN S
.. ... ... ... ... ... ... ...
886 10000000 0 2 ... 13.0000 NaN S
887 10000000 1 1 ... 30.0000 B42 S
888 10000000 0 3 ... 23.4500 NaN S
889 10000000 1 1 ... 30.0000 C148 C
890 10000000 0 3 ... 7.7500 NaN Q
[891 rows x 12 columns]
Update lists and dicts inside cells
from a_pandas_ex_old_school_for_loop import pd_add_old_school_for_loop
from random import randrange
pd_add_old_school_for_loop()
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
getrandomlist = lambda x: [
[[randrange(1, 100), randrange(1, 100)] * 1] * randrange(1, 10)
]
df["listtest"] = df.PassengerId.map(getrandomlist)
df
Out[4]:
PassengerId ... listtest
0 1 ... [[[88, 15], [88, 15], [88, 15], [88, 15]]]
1 2 ... [[[15, 63], [15, 63], [15, 63], [15, 63], [15,...
2 3 ... [[[46, 9], [46, 9], [46, 9], [46, 9]]]
3 4 ... [[[71, 12], [71, 12], [71, 12], [71, 12]]]
4 5 ... [[[55, 44], [55, 44], [55, 44], [55, 44], [55,...
.. ... ... ...
886 887 ... [[[81, 15], [81, 15], [81, 15], [81, 15], [81,...
887 888 ... [[[18, 70]]]
888 889 ... [[[16, 35]]]
889 890 ... [[[48, 54], [48, 54], [48, 54], [48, 54], [48,...
890 891 ... [[[11, 99], [11, 99], [11, 99], [11, 99], [11,...
for item in df.listtest.ds_set_values_with_loop():
try:
if item['get_value']() > 15:
item['set_value'](15000000)
except Exception:
continue
df
Out[6]:
PassengerId ... listtest
0 1 ... [[[15000000, 15], [15000000, 15], [15000000, 1...
1 2 ... [[[15, 15000000], [15, 15000000], [15, 1500000...
2 3 ... [[[15000000, 9], [15000000, 9], [15000000, 9],...
3 4 ... [[[15000000, 12], [15000000, 12], [15000000, 1...
4 5 ... [[[15000000, 15000000], [15000000, 15000000], ...
.. ... ... ...
886 887 ... [[[15000000, 15], [15000000, 15], [15000000, 1...
887 888 ... [[[15000000, 15000000]]]
888 889 ... [[[15000000, 15000000]]]
889 890 ... [[[15000000, 15000000], [15000000, 15000000], ...
890 891 ... [[[11, 15000000], [11, 15000000], [11, 1500000...
[891 rows x 13 columns]
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