Clearing certain data from column dataframe

Question

I have a pandas.Dataframe where after processing it looks like this:

Type|Fiscal quarter
A|FY18 Q3
A|FY18 Q4
A|FYn Q3

I would like to clear data where date = FYn type

So it will look like this:

Type|Fiscal quarter
A|FY18 Q3
A|FY18 Q4
A|

How do I do this?

muskrat · Accepted Answer · 2018-01-05 02:50:07Z

3

Pandas offers pd.replace, so you can use:

df['Fiscal quarter']=df['Fiscal quarter'].replace('FYn.*','',regex=True)

This will achieve the desired purpose using the regex FYn.*

answered Jan 5, 2018 at 2:50

muskrat

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Comments

Stephen Rauch · Accepted Answer · 2018-01-05 02:31:19Z

1

You can test the value of the Fiscal quarter column and use it to select the values to replace like:

Code:

df['Fiscal quarter'][df['Fiscal quarter'].str.startswith('FYn')] = ''

Test Code:

import pandas as pd

df = pd.DataFrame([
    ('A', 'FY18 Q3'),
    ('A', 'FY18 Q4'),
    ('A', 'FYn Q3'),
], columns=['Type', 'Fiscal quarter'])

print(df)

df['Fiscal quarter'][df['Fiscal quarter'].str.startswith('FYn')] = ''
print(df)

Results:

  Type Fiscal quarter
0    A        FY18 Q3
1    A        FY18 Q4
2    A         FYn Q3

  Type Fiscal quarter
0    A        FY18 Q3
1    A        FY18 Q4
2    A

answered Jan 5, 2018 at 2:31

Stephen Rauch♦

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Comments

rnso · Accepted Answer · 2018-01-05 04:21:20Z

0

One can use easily understandable for loop to check and change each row:

import pandas as pd
newvals = []                          # create new list for new dataframe
for v in df.values:                   # v will be as: ['A' 'FY18 Q3']
    if v[1].startswith("FYn"):        # remove entry if criteria satisfied
        v[1] = ""
    newvals.append(v)
df = pd.DataFrame(data=newvals, columns=df.columns)
print(df)

Output:

  Type Fiscal quarter
0    A        FY18 Q3
1    A        FY18 Q4
2    A

answered Jan 5, 2018 at 4:21

rnso

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