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This article shows you how to compare Pandas and data.table in Python. The content is concise and easy to understand. It will definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.

In this article, we will compare Pandas with data.table, which are the longest-lasting data analysis packages for Python and R. We won't say that one is better, but our focus here is to demonstrate how these two libraries provide efficient and flexible methods for data processing.

The examples we will introduce are common data analysis and manipulation operations. As a result, you may use them often.

We will use the Melbourne housing data set available on Kaggle as an example. I will use Google Colab (Pandas) and RStudio (data.table) as the open environment. Let's first import the library and read the dataset.

# pandas import pandas as pd melb = pd.read_csv ("/ content/melb_data.csv") # data.table library (data.table) melb 1000000) & (melb.Type = = "h")] # data.table subset 1000000 & Type = = "h"]

For pandas, we provide the name of the dataframe to select the column to filter. On the other hand, using only column names for data.table is sufficient.

Example 3

A very common function used in data analysis is the groupby function. It allows you to compare different values in classified variables based on some numerical metrics.

For example, we can calculate the average house prices in different regions. To make the example more complex, we also apply a filter to the house type.

# pandas melb [melb.Type = = "u"] .groupby ("Regionname"). Agg (avg_price = ("Price", "mean")) # data.table melb [Type = = "u",. (avg_price = mean (Price)), by= "Regionname"]

Pandas uses the groupby function to perform these operations. For data.table, this is relatively simple because we only need to use the by parameter.

Example 4

Let's discuss the previous example further. We have worked out the average price of the house, but we don't know the number of houses in each area.

Both libraries allow multiple aggregations to be applied in a single operation. We can also sort the results in ascending or descending order.

# pandas melb [melb.Type = = "u"] .groupby ("Regionname"). Agg (avg_price = ("Price", "mean"), number_of_houses= ("Price", "count"). Sort_values (by= "avg_price", ascending=False) # data.table > melb [Type = = "u",. (avg_price = mean (Price), number_of_houses=.N), by= "Regionname"] [order (- avg_price)]

We use the count function to get the number of houses in each group. ". N" can be used as the count function in data.table.

By default, both libraries sort the results in ascending order. Collation is controlled by the ascending parameter in pandas. Use the minus sign in data.table to get the descending result.

Example 5

In the final example, we will see how to change the column name. For example, we can change the name of the type and distance column.

Type: HouseType

Distance: DistanceCBD

The distance column in the dataset represents the distance to the central business district (CBD), so it is best to provide this information in the column name.

# pandas melb.rename (columns= {"Type": "HouseType", "Distance": "DistanceCBD"}, inplace=True) # data.table setnames (melb, c ("Type", "Distance"), c ("HouseType", "DistanceCBD")

For pandas, we pass a dictionary that maps changes to the rename function. The inplace parameter is used to save the results in the original data frame.

For data.table, we use the setnames function. It takes three parameters, namely the table name, the column name to change, and the new column name.

We compared five common examples of pandas and data.table during data analysis operations. Both libraries provide simple and effective ways to accomplish these tasks.

In my opinion, data.table is a little simpler than pandas.

It is important to point out that the examples we have done in this article represent only a small portion of the capabilities of these libraries. They provide many functions and methods to perform more complex operations.

The above is how to compare Pandas and data.table in Python. Have you learned any knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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