Handling Weekly Data from Monthly Data in Pandas

In this article, we will explore how to split monthly data into weekly data and fill each week’s row with the same monthly value.

Introduction

When working with time-series data, it is common to have monthly data that needs to be converted into weekly data for analysis or other purposes. In this article, we will discuss how to achieve this using pandas in Python.

Preparing the Data

First, let us prepare our data by converting the datetime columns to datetime format and creating a dictionary from the start time column.

# Import necessary libraries
import pandas as pd

# Load the data
df = pd.read_excel("file")

# Convert the datetime columns to datetime format
df[['starting date', 'ending date']] = df[['starting date', 'ending date']].apply(pd.to_datetime)

# Create a dictionary from the start time column
d = dict(zip(df['starting date'], df['data']))

Creating Weekly Intervals

Next, we will use pd.date_range() to create a dataframe having weekly intervals of the start time.

# Use pd.date_range() to create a dataframe having weekly intervals of the start time
df_new = pd.DataFrame(pd.date_range(df['starting date'].iloc[0], df['ending date'].iloc[-1], freq='W-TUE'), columns=['StartDate'])

Creating End Dates

We will also use pd.date_range() to create a dataframe having weekly intervals of the end time.

# Use pd.date_range() to create a dataframe having weekly intervals of the end time
df_new['EndDate'] = pd.date_range(df['starting date'].iloc[0], df['ending date'].iloc[-1], freq='W-MON')

Mapping Data

We will map the data column based on the start time and use ffill() to fill in the missing values.

# Map the data column based on the start time
df_new['data'] = df_new['StartDate'].map(d).fillna(method='ffill')

print(df_new)

The Result

The resulting dataframe will have the weekly intervals of the start and end times, along with the corresponding monthly values.

# The resulting dataframe
   StartDate    EndDate  data
0 2013-01-01 2013-01-07  20.0
1 2013-01-08 2013-01-14  20.0
2 2013-01-15 2013-01-21  21.0
3 2013-01-22 2013-01-28  21.0

Conclusion

In this article, we demonstrated how to split monthly data into weekly data and fill each week’s row with the same monthly value using pandas in Python. We created a dictionary from the start time column, used pd.date_range() to create weekly intervals of both the start and end times, and then mapped the data column based on the start time.

Additional Tips

• When working with datetime columns, make sure to convert them to datetime format using pd.to_datetime().
• Use df.iloc[] to access specific rows or columns in a dataframe.
• The freq parameter in pd.date_range() specifies the frequency of the date range. Common frequencies include ‘W-TUE’ for Tuesday, ‘M’ for month, and ‘D’ for day.

Example Use Cases

• This code can be used to convert monthly sales data into weekly sales data for analysis or other purposes.
• It can also be used to create a schedule of events based on start times.

Future Development

• In future articles, we will explore more advanced techniques for handling time-series data in pandas.
• We will discuss how to use other libraries such as NumPy and SciPy for numerical computations.

Last modified on 2023-11-23