CODEWITHSUNDEEP
pythonpython-3.xpandasdataframe
MHanu
MHanu

Reputation: 335

Change value of uneven to specific even numbers

I have a dataframe with mixed data types and I would like to change the values of str cells (each consisting of two letters plus three numbers) so that uneven number become even numbers but the number decreases. AB123 should become AB122 while not changing the letter before it.

Here is an example dataframe with mixed types:

df = pd.DataFrame({'Opportunity':['AB122','AB123','AB125', 'AB124'],
           'Quantity': [2, 3, 4, 1],
           'Member': ["AACC", "AACC", "AACC", 'DDEE']})


print (df)
  Opportunity  Quantity Member
0       AB122         2   AACC
1       AB123         3   AACC
2       AB121         4   AACC
3       AB120         1   DDEE

I would like the outcome to be:

print (df2)

  Opportunity  Quantity Member
0       AB122         2   AACC
1       AB122         3   AACC
2       AB120         4   AACC
3       AB120         1   DDEE

Upvotes: 6

Views: 136

Answers (4)

iamklaus
iamklaus

Reputation: 3770

df['Opportunity'] = df.Opportunity.apply(lambda x: x[:-3]+str(int(x[-3:]) - 1) if int(x[-3:]) % 2 != 0 else x)

Opportunity  Quantity Member
0       AB122         2   AACC
1       AB122         3   AACC
2       AB120         4   AACC
3       AB120         1   DDEE

Upvotes: 0

yatu
yatu

Reputation: 88266

Here's one way slicing with pandas's str accessor, subtracting the mod by 2 to the integer part and concatenating back to the column using str.cat:

d = df.Opportunity.str[-2:].astype(int)
df['Opportunity'] = df.Opportunity.str[:3].str.cat(d.sub(d.mod(2)).astype(str))

    Opportunity  Quantity Member
0       AB122         2   AACC
1       AB122         3   AACC
2       AB120         4   AACC
3       AB120         1   DDEE

Input dataframe:

print(df)

Opportunity  Quantity Member
0       AB122         2   AACC
1       AB123         3   AACC
2       AB121         4   AACC
3       AB120         1   DDEE

Upvotes: 2

U13-Forward
U13-Forward

Reputation: 71600

Try:

df['Opportunity'] = df['Opportunity'].str[:2] + np.where(df['Opportunity'].str[2:].astype(int) % 2, df['Opportunity'].str[2:].astype(int).sub(1).astype(str), df['Opportunity'].str[2:])

And now:

print(df)

Is:

  Member Opportunity  Quantity
0   AACC       AB122         2
1   AACC       AB122         3
2   AACC       AB120         4
3   DDEE       AB120         1

Upvotes: 3

adnanmuttaleb
adnanmuttaleb

Reputation: 3624

df = pd.DataFrame({'Opportunity':['AB122','AB123','AB125', 'AB124'],
               'Quantity': [2, 3, 4, 1],
               'Member': ["AACC", "AACC", "AACC", 'DDEE']})

even = lambda x: int(x) % 2 == 0
df['Opportunity'] = df['Opportunity'].apply(lambda x: x[:2] + (x[2:] if even(x[2:]) else str(int(x[2:]) -1)))

print(df)

output:

  Opportunity  Quantity Member
0       AB122         2   AACC
1       AB122         3   AACC
2       AB124         4   AACC
3       AB124         1   DDEE

Upvotes: 0

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