I have some tables from the Bureau of Labor Statistics that I converted to cvs files in Python. The 'Item' column has some rows with multiple '.' . I'm trying to iterate through these rows and replace these '.' with '' .
I've tried:
for row in age_df_1989['Item']:
if '.' in row:
age_df_1989['Item'].replace('.','')
Any ideas on what I can do for this?
No assigning age_df_1989['Item'].replace('.','') to a variable won't change the original data, you need to do this:
for row in age_df_1989['Item']:
if '.' in row:
row['Item'] = row['Item'].replace('.','')
Try apply
age_df_1989['Item'] = age_df_1989['Item'].apply(lambda x: x.replace('.', '')
Simple & faster than a for loop
Use the vectorised str method replace: This is much faster than the iterrows or for loop or apply option.
You can do something as simple as
df['column name'] = df['column name'].str.replace('old value','new value')
For your example, do this:
age_df_1989['Item'] = age_df_1989['Item'].str.replace('.', '')
Here's an example output of this:
c = ['Name','Item']
d = [['Bob','Good. Bad. Ugly.'],
['April','Today. Tomorrow'],
['Amy','Grape. Peach. Banana.'],
['Linda','Pink. Red. Yellow.']]
import pandas as pd
age_df_1989 = pd.DataFrame(d, columns = c)
print (age_df_1989)
age_df_1989['Item'] = age_df_1989['Item'].str.replace('.', '')
print (age_df_1989)
Dataframe: age_df_1989 : Original
Name Item
0 Bob Good. Bad. Ugly.
1 April Today. Tomorrow
2 Amy Grape. Peach. Banana.
3 Linda Pink. Red. Yellow.
Dataframe: age_df_1989 : After the replace command
Name Item
0 Bob Good Bad Ugly
1 April Today Tomorrow
2 Amy Grape Peach Banana
3 Linda Pink Red Yellow
