@bgarcial wrote:
I have a dataset with a date range of January 12th to August 3rd of 2018 with some values:
The dimensionality of
my_df
DataFrame is:my_df.shape (9752, 2)
Each row data have a frequency of half hour
The first row begins at
2018-01-12
my_df.iloc[0] Date: 2018-01-12 00:17:28 Value 1 Name: 0, dtype: object
And the last row ending at
2018-08-03
my_df.tail(1) Date: Value 9751 2018-08-03 23:44:59 1
My objective is to select the data rows corresponding to each day and export it to a comma separated values file.
To get only the January 12th data and save to readable file, I perform:
# Selecting data value of each day my_df_Jan12 = my_df[(my_df['Fecha:']>='2018-01-12 00:00:00') & (my_df['Fecha:']<='2018-01-12 23:59:59') ] my_df_Jan12.to_csv('Data_Jan_12.csv', sep=',', header=True, index=False)
From January 12 to August 03 there are 203 days (28 weeks)
I don’t want to perform this query by each day of a manual way, then I am trying the following basic analysis:
- I need generate 203 files (1 file by each day)
- The day on January starting on 12 (January 12)
- January is a first month (01) and August is the eighth month(08)
Then:
- I need iterate over the 203 days totality
- and is necessary in each date row value check
the month and day value date with the order to check the change of
each one of themAccording to the above, I am trying this approach:
# Selecting data value of each day (203 days) for i in range(203): for j in range(1,9): # month for k in range(12,32): # days of the month values = my_df[(my_df['Fecha:']>='2018-0{}-{} 00:00:00'.format(j,k)) & (my_df['Fecha:']<='2018-0{}-{} 23:59:59'.format(j,k))] values.to_csv('Values_day_{}.csv'.format(i), sep=',', header=True, index=False)
But I have the problem in the sense of when I iterate of
range(12,32)
in the days of the months, thisrange(12,32)
only apply to first January month, I think so …Finally, I get 203 empty comma separated value files, due to something I am doing wrong …
How to can I address this small challenge of the suited way?
Any orientation is highly appreciated
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