CODEWITHSUNDEEP
pythonpandasextract
bgarcial
bgarcial

Reputation: 3193

Extracting data belonging to a day from a given range of dates on a dataset

I have a data set with a date range from January 12th to August 3rd of 2018 with some values:

enter image description here

The dimensionality of my_df DataFrame is:

my_df.shape 
(9752, 2)

Each row contains half hour frequency

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 goal is to select the data rows corresponding to each day and export it to a CSV 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 manually, then I am trying the following basic analysis:

Then:

According 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, this range(12,32) only apply to first January month, I think so ...

Finally, I get 203 empty CSV 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

Upvotes: 3

Views: 1184

Answers (2)

piRSquared
piRSquared

Reputation: 294288

groupby

for date, d in df.groupby(pd.Grouper(key='Date', freq='D')):
  d.to_csv(f"Data_{date:%b_%d}.csv", index=False)

Notice I used an f-string which is Python 3.6+
Otherwise, use this

for date, d in df.groupby(pd.Grouper(key='Date', freq='D')):
  d.to_csv("Data_{:%b_%d}.csv".format(date), index=False)

Consider the df

df = pd.DataFrame(dict(
    Date=pd.date_range('2010-01-01', periods=10, freq='12H'),
    Value=range(10)
))

Then

for date, d in df.groupby(pd.Grouper(key='Date', freq='D')):
  d.to_csv(f"Data_{date:%b_%d}.csv", index=False)

And verify

from pathlib import Path

print(*map(Path.read_text, Path('.').glob('Data*.csv')), sep='\n')

Date,Value
2010-01-05 00:00:00,8
2010-01-05 12:00:00,9

Date,Value
2010-01-04 00:00:00,6
2010-01-04 12:00:00,7

Date,Value
2010-01-02 00:00:00,2
2010-01-02 12:00:00,3

Date,Value
2010-01-01 00:00:00,0
2010-01-01 12:00:00,1

Date,Value
2010-01-03 00:00:00,4
2010-01-03 12:00:00,5

Upvotes: 3

Vishnu Kunchur
Vishnu Kunchur

Reputation: 1726

Something like this? I renamed your original column of Date: to Timestamp. I am also assuming that the Date: Series you have is a pandas DateTime series.

my_df.columns = ['Timestamp', 'Value']
my_df['Date'] = my_df['Timestamp'].apply(lambda x: x.date())
dates = my_df['Date'].unique()
for date in dates:
    f_name = str(date) + '.csv'
    my_df[my_df['Date'] == date].to_csv(f_name)

Upvotes: 3

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