Pandas Groupby() – Combine all Values Into One Set Shared with the Same Index Key Using Python

In this piece, I will share introduce Pandas GroupBy(), and go through how to combine the value into one set with a shared key, or column value. For example, if your Google advertising campaign name is shared with different data sets such as data from daily, weekly or monthly, and so on and so forth, here is a way to consolidate them into one set for easy fetching, using, and applying them in web application interactions.

pandas groupby

In this piece, I will share introduce Pandas GroupBy(), and go through how to combine the value into one set with a shared key, or column value. For example, if your Google advertising campaign name is shared with different data sets such as data from daily, weekly or monthly, and so on and so forth, here is a way to consolidate them into one set for easy fetching, using, and applying them in web application interactions.

Table of Contents on Pandas Groupby()

Pandas Groupby() & Apply(lambda)

A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.

For example, here is a sample of a stock symbol, which is BILL. Basically, every day has new updates from different media. If you like to set BILL this keyword is a key to fetch any news-related BILL. groupby() can be used in this case by combining the symbol column into one.

df.groupby('Symbol')

pandas groupby

Then, we also need to select BILL’s data you like to add on and applied to this new packed dataset under a single key – BILL. Then, we can use apply() and lambda functions

Apply()

Apply function func group-wise and combine the results together. The function passed to apply must take a data frame as its first argument and return a DataFrame, Series, or scalar. apply will then take care of combining the results back together into a single data frame or series. apply is therefore a highly flexible grouping method.

In this case, we use apply() to tell which column of data would be under the same key BILL

Lambda

pandas.groupby(“Symbol”).apply(lambda x: x[])) can be used with python lambda to execute expressions. A lambda function in python is a small anonymous function that can take any number of arguments and execute an expression.

In this case, the list value in lambda is the selected columns you like to add to the key BILL. Here is the code sample as follows:

Abc = df.groupby('Symbol').apply(lambda x: x[['News Publish Date','News Title','News Link','News Source','E2D Updated Date']]

Use to_json(), reset_index(), set_index() to set a key and rename the packed dataset

The main purpose of consolidating the entry point BILL using groupby().apply(lambda) is because of easily fetching all data related to this symbol. This approach notably is popular if you like to convert them into JSON data format.

For this purpose, these functions necessarily are used to convert these data.

Cde = abc.to_json(orient=’records’)

This variable is to turn the dataset under the symbol into JSON format

Efg = Cde.reset_index().rename(columns={0:'NewsData'})

To create a new name for the stock news related to this Symbol, we can reset the index and rename the index into a new name

Xyz = Efg.set_index('Symbol', inplace=True)

Last but not least, we select the key as the JSON key as well by setting it as the index element. Thus, as long as users call the unique stock symbol name in JSON, all related news data can be fetched.

Full Python scripts of Pandas Groupby(), Lambda, reset_index and set_index

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