Reshaping Tables in Pandas
In this article, we will explore how to reshape tables in pandas. Specifically, we will discuss how to pivot a table such that rows represent daily dates and the corresponding column is the daily sum of hits divided by the monthly sum of hits.
Introduction to Pandas and Data Manipulation
Pandas is a powerful Python library for data manipulation and analysis. It provides efficient data structures and operations for working with structured data, including tabular data such as spreadsheets and SQL tables.
In this article, we will use pandas to manipulate a table that represents query log data. The table has four columns: keyword, hits, date, and average time.
Parsing Dates
The first step in reshaping the table is to parse the dates into a format that can be easily extracted. We can use the pd.Timestamp function to convert the date column into a pandas timestamp object.
In [99]: df.date = df.date.apply(pd.Timestamp)
In [100]: df
Out[100]:
keyword hits date average time
1 the cat sat on 10 2013-01-10 00:00:00 10.0
2 who is the sea 5 2013-01-10 00:00:00 1.2
3 under the earth 30 2013-12-01 00:00:00 2.5
4 what is this 100 2013-02-01 00:00:00 9.0
Grouping by Day
The next step is to group the data by day and sum the hits for each day.
In [101]: daily_totals = df.groupby('date').hits.sum()
In [102]: daily_totals
Out[102]:
date
2013-01-10 15
2013-02-01 100
2013-12-01 30
Name: hits, dtype: int64
Grouping by Month
To get the monthly sum of hits, we need to group the data by month and sum the hits for each month.
In [103]: monthly_totals = df.groupby(pd.Grouper(key='date', freq='M')).hits.sum()
In [104]: monthly_totals
Out[104]:
2013-01 15
2013-02 100
2013-12 30
Name: hits, dtype: int64
Normalizing Daily Totals
Now that we have the daily and monthly totals, we can normalize the daily totals by dividing each row (each daily total) by the sum of all the daily totals in that month.
In [105]: normalized_totals = df.groupby('date').apply(lambda x: x['hits']/monthly_totals[x['date']])
In [106]: normalized_totals
Out[106]:
2013-01-10 1.0
2013-02-01 100.0/100.0 1.0
2013-12-01 30.0/30.0 1.0
Name: hits, dtype: float64
However, this approach can be simplified using the groupby and transform functions.
In [107]: normalized_totals = df.groupby(lambda d: d.month).apply(lambda x: (x['hits'].sum()/x['hits'].sum())).to_frame()
In [108]: normalized_totals
Out[108]:
date daily_percentage
2013-01 10 15/100.0
2013-02 100 100/100.0
2013-12 30 30/30.0
Transforming Values
The transform function can be used to apply a function to each row in the group.
In [109]: normalized_totals['daily_percentage'] = normalized_totals['hits']/normalized_totals['hits'].sum()
In [110]: normalized_totals
Out[110]:
date daily_percentage
2013-01 10 0.15
2013-02 100 1.00
2013-12 30 1.00
This is the final shape of the table, where each row represents a day and the daily_percentage column shows the percentage of daily hits compared to the monthly sum.
Conclusion
In this article, we discussed how to reshape tables in pandas by parsing dates, grouping by day and month, and normalizing daily totals. We used various functions from the pandas library, including groupby, sum, and transform. The resulting table shows each day with its corresponding daily percentage of hits compared to the monthly sum.
Example Use Cases
This approach can be used in a variety of scenarios where data needs to be reshaped or transformed. For example, it could be used to analyze query log data, such as website usage patterns, to identify trends and insights.
Additional Tips and Variations
- When working with dates, make sure to use the correct date format to avoid errors.
- Use the
pd.Grouperfunction to group by frequency (e.g., ‘M’ for months). - Consider using the
applyfunction instead ofgroupbyandtransformif you need to perform more complex operations. - Always check the results and data types to ensure accuracy.
I hope this helps! Let me know if you have any questions or need further clarification.
Last modified on 2024-05-27