I have a data frame with different columns name (asset.new, few, value.issue, etc). And I want to change some characters or symbols in the name of columns. I can do it in this form:
df.columns = df.columns.str.replace('.', '_')
df.columns = df.columns.str.replace('few', 'LOW')
df.columns = df.columns.str.replace('value', 'PRICE')
....
But I think it should have a better and shorter way.
You can create a dictionary with the actual character as a key and the replacement as a value and then you iterate through your dictionary:
df = pd.DataFrame({'asset.new':[1,2,3],
'few':[4,5,6],
'value.issue':[7,8,9]})
replaceDict = { '.':'_', 'few':'LOW', 'value':'PRICE'}
for k, v in replaceDict.items():
df.columns = [c.replace(k, v) for c in list(df.columns)]
print(df)
output:
asset_new LOW PRICE_issue
1 4 7
2 5 8
3 6 9
or:
df.columns = df.columns.to_series().replace(["\.","few","value"],['_','LOW','PRICE'],regex=True)
Produces the same output.
Use Series.replace with dictionary - also necessary escape . because special regex character:
d = { '\.':'_', 'few':'LOW', 'value':'PRICE'}
df.columns = df.columns.to_series().replace(d, regex=True)
More general solution with re.esape:
import re
d = { '.':'_', 'few':'LOW', 'value':'PRICE'}
d1 = {re.escepe(k): v for k, v in d.items()}
df.columns = df.columns.to_series().replace(d1, regex=True)
