I've been struggling with one problem for two days that I have with Python and the Pandas package. I am creating a dataframe with filtered data from another csv and then want to export it to csv again. However, I have a problem because I do not know how to add another value to a specific cell without removing the previous one so that it looks more or less like this in CSV. So that I could add this letter b to the second column and the first row where the letter a already exists
You can use this code It edits all of the rows if you want else you want to edit just first row you can remove for loop and use i=0
for i in df.index:
if df['B'][i].find('a')>=0:
df['B'][i] += ', b'
df_10 = pd.DataFrame({'A': ['xx', '--- bios ---', 'yy', 'zz', '--- os ---'],'B': ['rat', '', 'winter', 'host','']})
I create such a data frame as add to column A and line 2, for example the value "new"
This is how I would approach the problem:
Code:
df = pd.DataFrame(
data={
'A': [1, 2, 3],
'B': ['a', 'a', 'b'],
}
)
Result
df
A B
0 1 a
1 2 a
2 3 b
Method: https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html
Code:
# Condition: the character 'a' has to be present
df['B'].apply(lambda x: x + ', b' if 'a' in x else x)
Result:
df
A B
0 1 a, b
1 2 a, b
2 3 b
This will work for the entire 2nd column (in this example, column ‘B’).
If you’d like to apply this rule only to a specific row of the dataframe, I would only add the following:
Code:
# Condition: the character 'a' has to be present on a selecteed row
row = 0 ## row where you'd like to apply the change
df.loc[row, ['B']] = df.loc[row, ['B']].apply(lambda x: x + ', b' if 'a' in x else x)
Result:
df
A B
0 1 a, b
1 2 a
2 3 b
Hope this solutions helps!
This is very very faster and better
def f(s):
if s.find('a')>=0:
return s+', b'
return s
df['B']=df['B'].apply(func=f)