CODEWITHSUNDEEP
pythonpandasdataframemultiple-columns
BEAst
BEAst

Reputation: 219

how to add a column with different values

I have a question regarding adding a column table with different type of values: e.g. the length of the table is 10 rows. The first be given 1 of January, second row 1 of February, 3 row -1 of march etc.

Is there a function that enables this instead of writing everything "by hand".

Thanks in advance.

Upvotes: 0

Views: 323

Answers (1)

jezrael
jezrael

Reputation: 863166

I think you need helper DataFrame and then join column to original.

Notice: index of df is default, it means unique, monotonic (0,1,2...)

#create date range longer as max length of original df
df1 = pd.DataFrame({'rng': pd.date_range('2016-01-01', periods=1000)})
#convert to string and remove trailing 0
df1['new'] = df1['rng'].dt.strftime('%d of %B').str.lstrip('0')
#extract year, month, days
df1 = df1.assign(year=df1['rng'].dt.year,month=df1['rng'].dt.month, day=df1['rng'].dt.day)
#sorting and create default index (0,1,2)
df1 = df1.sort_values(['year','day','month']).reset_index(drop=True)

print (df1.head())
         rng            new  day  month  year
0 2016-01-01   1 of January    1      1  2016
1 2016-02-01  1 of February    1      2  2016
2 2016-03-01     1 of March    1      3  2016
3 2016-04-01     1 of April    1      4  2016
4 2016-05-01       1 of May    1      5  2016

df = pd.DataFrame({'A':list('abcdef'),
                   'B':[4,5,4,5,5,4]})

df = df.join(df1['new'])
print(df)
   A  B            new
0  a  4   1 of January
1  b  5  1 of February
2  c  4     1 of March
3  d  5     1 of April
4  e  5       1 of May
5  f  4      1 of June

Upvotes: 2

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