Python - Aggregate by month and calculate average

Question

I have a csv which looks like this:

Date,Sentiment
2014-01-03,0.4
2014-01-04,-0.03
2014-01-09,0.0
2014-01-10,0.07
2014-01-12,0.0
2014-02-24,0.0
2014-02-25,0.0
2014-02-25,0.0
2014-02-26,0.0
2014-02-28,0.0
2014-03-01,0.1
2014-03-02,-0.5
2014-03-03,0.0
2014-03-08,-0.06
2014-03-11,-0.13
2014-03-22,0.0
2014-03-23,0.33
2014-03-23,0.3
2014-03-25,-0.14
2014-03-28,-0.25
etc

And my goal is to aggregate date by months and calculate average of months. Dates might not start with 1. or January. Problem is that I have a lot of data, that means I have more years. For this purpose I would like to find the soonest date (month) and from there start counting months and their averages. For example:

Month count, average
1, 0.4 (<= the earliest month)
2, -0.3
3, 0.0
...
12, 0.1
13, -0.4 (<= new year but counting of month is continuing)
14, 0.3

I'm using Pandas to open csv

data = pd.read_csv("pks.csv", sep=",")

so in data['Date'] I have dates and in data['Sentiment'] I have values. Any idea how to do it?

Stigmatism

Answer 1

Probably the simplest approach is to use the resample command. First, when you read in your data make sure you parse the dates and set the date column as your index (ignore the StringIO part and the header=True ... I am reading in your sample data from a multi-line string):

>>> df = pd.read_csv(StringIO(data),header=True,parse_dates=['Date'],
                     index_col='Date')
>>> df

            Sentiment
Date
2014-01-03       0.40
2014-01-04      -0.03
2014-01-09       0.00
2014-01-10       0.07
2014-01-12       0.00
2014-02-24       0.00 
2014-02-25       0.00
2014-02-25       0.00
2014-02-26       0.00
2014-02-28       0.00
2014-03-01       0.10
2014-03-02      -0.50
2014-03-03       0.00
2014-03-08      -0.06
2014-03-11      -0.13
2014-03-22       0.00
2014-03-23       0.33
2014-03-23       0.30
2014-03-25      -0.14
2014-03-28      -0.25


>>> df.resample('M').mean()

            Sentiment
2014-01-31      0.088
2014-02-28      0.000
2014-03-31     -0.035

And if you want a month counter, you can add it after your resample:

>>> agg = df.resample('M',how='mean')
>>> agg['cnt'] = range(len(agg))
>>> agg

            Sentiment  cnt
2014-01-31      0.088    0
2014-02-28      0.000    1
2014-03-31     -0.035    2

You can also do this with the groupby method and the TimeGrouper function (group by month and then call the mean convenience method that is available with groupby).

>>> df.groupby(pd.TimeGrouper(freq='M')).mean()

            Sentiment
2014-01-31      0.088
2014-02-28      0.000
2014-03-31     -0.035

Thomism

using new syntax for resample; df.resample('M').mean() – Medium

if the date is not in index rather as a column and the column name is "date", then df.resample('M', on="date").mean() – Rathbone

Answer 2

To get the monthly average values of a Data Frame when the DataFrame has daily data rows 'Sentiment', I would:

1. Convert the column with the dates , df['dates'] into the index of the DataFrame df: df.set_index('date',inplace=True)
2. Then I'll convert the index dates into a month-index: df.index.month
3. Finally I'll calculate the mean of the DataFrame GROUPED BY MONTH: df.groupby(df.index.month).Sentiment.mean()

I go slowly throw each step here:

Generation DataFrame with dates and values

• You need first to import Pandas and Numpy, as well as the module datetime

  from datetime import datetime
  

• Generate a Column 'date' between 1/1/2019 and the 3/05/2019, at week 'W' intervals. And a column 'Sentiment'with random values between 1-100:

  date_rng = pd.date_range(start='1/1/2018', end='3/05/2018', freq='W')
  df = pd.DataFrame(date_rng, columns=['date'])
  df['Sentiment']=np.random.randint(0,100,size=(len(date_rng)))
  

• the df has two columns 'date' and 'Sentiment':

          date  Sentiment
  0 2018-01-07         34
  1 2018-01-14         32
  2 2018-01-21         15
  3 2018-01-28          0
  4 2018-02-04         95
  5 2018-02-11         53
  6 2018-02-18          7
  7 2018-02-25         35
  8 2018-03-04         17
  

  Set 'date'column as the index of the DataFrame:

  df.set_index('date',inplace=True)
  

• df has one column 'Sentiment' and the index is 'date':

              Sentiment
  date                 
  2018-01-07         34
  2018-01-14         32
  2018-01-21         15
  2018-01-28          0
  2018-02-04         95
  2018-02-11         53
  2018-02-18          7
  2018-02-25         35
  2018-03-04         17
  

Capture the month number from the index

    months=df.index.month

Obtain the mean value of each month grouping by month:

    monthly_avg=df.groupby(months).Sentiment.mean()

The mean of the dataset by month 'monthly_avg' is:

    date
    1    20.25
    2    47.50
    3    17.00

Lagrange