CODEWITHSUNDEEP
pythonpandaspandas-groupbydata-science
Induraj PR
Induraj PR

Reputation: 304

Split and create new dataframe based on WeekDays from existing dataframe

I need to split the dataframe based on weekdays,
The actual dataframe looks like this,

df = pd.DataFrame({'values': [10,5,30,44,52,6,7,85,9,1,1,1,13,14,1,16]})
df['weekdays'] = ['Monday','Tuesday','Wednesay','Thursday','Friday','saturday','sunday',
        'Tuesday','Wednesay','Thursday','Friday','saturday','sunday',
      'Monday','Tuesday','Wednesday']


    values   weekdays
0       10     Monday
1        5    Tuesday
2       30   Wednesay
3       44   Thursday
4       52     Friday
5        6   saturday
6        7     sunday
7       85    Tuesday
8        9   Wednesay
9        1   Thursday
10       1     Friday
11       1   saturday
12      13     sunday
13      14     Monday
14       1    Tuesday
15      16  Wednesday

how to split the dataframe based on weekdays like shown below? I Tried splitting the dataframe as 7 rows each, but this adds data from consequtive week, so how not to add data from consequtive week and split the dataframe exactly like this below? Thanks in advance for help.

new_df_1

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new_df_2

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new_df_3

enter image description here

Upvotes: 1

Views: 180

Answers (1)

Shubham Sharma
Shubham Sharma

Reputation: 71687

Create a grouper grp using Series.shift + Series.eq + Series.cumsum, then use this grouper to group the dataframe and use a dict comprehension to store each of grouped frame in dictionary:

grp = df['weekdays'].shift().eq('sunday').cumsum()
dfs = {f'df{k+1}': g for k, g in df.groupby(grp)}

Result:

print(dfs['df1'])
   values  weekdays
0      10    Monday
1       5   Tuesday
2      30  Wednesay
3      44  Thursday
4      52    Friday
5       6  saturday
6       7    sunday

print(dfs['df2'])
    values  weekdays
7       85   Tuesday
8        9  Wednesay
9        1  Thursday
10       1    Friday
11       1  saturday
12      13    sunday

print(dfs['df3'])
    values   weekdays
13      14     Monday
14       1    Tuesday
15      16  Wednesday

Upvotes: 3

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