I have a trouble to clean my data because some values were not input correctly.
import pandas as pd
data = [[1, 2], [2, 4], [3, 6], [4, 8], [5, 10]]
index = ['100: Test', '100: test', '101: FOO', '102: WWW', '101: foo foo']
columns = ['column1', 'column2']
df = pd.DataFrame(data, index=index, columns=columns)
print(df)
## Current output!!!!
# column1 column2
#100: Test 1 2
#100: test 2 4
#101: FOO 3 6
#102: WWW 4 8
#101: foo foo 5 10
## DO SOMETHING!!!!
print(df)
## Expected output!!!!
# column1 column2
#100: Test 2 4
#101: FOO 8 16
#102: WWW 4 8
My DataFrame.index
consists of "ID" + "Name". However, names are not correct, so one ID may show up in more than one row.
Two requests
I tried to use groupby
function, but it doesn't seem to have regex
compatibility.
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html
df2 = df.groupby(level=0).sum()
print(df2)
## Output
# column1 column2
#100: Test 1 2
#100: test 2 4
#101: FOO 3 6
#101: foo foo 5 10
#102: WWW 4 8
Python 3.10.5 Pandas 1.4.3
Your expected output for Test
does not reflect that you are trying to do a summation, but from what I can gather this is what you want. groupby
can take a function or a mapping or even a series as the by
argument. Here, you just want the lowercase version of the index:
df.groupby(df.index.str.lower()).sum()
which gives
column1 column2
100: test 3 6
101: foo 8 16
102: www 4 8
Here, what I've done is passed it the lowercase index, and it simply groups the rows based on matching elements in the series.
Based on the updated question, to match the numbers, you can use regular expressions:
df.groupby(df.index.str.extract(r"(\d+):", expand=False)).sum()
which gives
column1 column2
100 3 6
101 8 16
102 4 8
It isn't clear what would take precedence 101: foo foo
or 101: FOO
, it seems like the numbers here are the important part regardless.
import numpy as np
import pandas as pd
# Data Import
data = [[1, 2], [2, 4], [3, 6], [4, 8], [5, 10]]
index = ['100: Test', '100: test', '101: FOO', '102: WWW', '101: foo']
columns = ['column1', 'column2']
df = pd.DataFrame(data, index=index, columns=columns)
# Data Pre-process
df.reset_index(inplace=True)
df.rename(columns={'index':'ID_Name'},inplace=True)
df['ID'] = df['ID_Name'].str.split(':').str[0]
df.sort_values(['ID','ID_Name'],inplace=True)
df_group = df.groupby(['ID'])[['column1','column2']].sum().reset_index()
df_group
df = pd.merge(df,df_group,how='left',left_on='ID',right_on='ID')
df_final = df.groupby(['ID']).first()
# Data Clean Process
df_final.rename(columns={'column1_y':'column1','column2_y':'column2'},inplace= True)
df_final.drop(['column1_x','column2_x'],axis = 1 , inplace=True)
# Output Display
df_final
Hi Dmjy,
I have attached the code for you, please try from your side, and if you still have any question please let me know
Thanks Leon
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