This article discusses how to improve the performance of Pandas operations by using vectorization with NumPy. It highlights alternatives to the apply() method on larger dataframes and provides examples of using NumPy's lesser-known methods like where and select to handle complex if/then/else conditions efficiently.
The article explores 11 essential tips for leveraging the full potential of the Pandas library to boost productivity and streamline workflows in handling and analyzing complex datasets. It uses a real-world dataset from Kaggle's Airbnb listings to illustrate techniques such as chunked processing and parallel execution.
These one-liners provide quick and effective ways to assess the quality and consistency of the data within a Pandas DataFrame.
| Code Snippet | Explanation |
| --- | --- |
| `df.isnull().sum()` | Counts the number of missing values per column. |
| `df.duplicated().sum()` | Counts the number of duplicate rows in the DataFrame. |
| `df.describe()` | Provides basic descriptive statistics of numerical columns. |
| `df.info()` | Displays a concise summary of the DataFrame including data types and presence of null values. |
| `df.nunique()` | Counts the number of unique values per column. |
| `df.apply(lambda x: x.nunique() / x.count() * 100)` | Computes the percentage of unique values for each column. |
| `df.isin( value » ).sum()` | Counts the number of occurrences of a specific value across all columns. |
| `df.applymap(lambda x: isinstance(x, type_to_check)).sum()` | Counts the number of values of a specific type (e.g., int, str) per column. |
| `df.dtypes` | Lists the data type for each column in the DataFrame. |
| `df.sample(n)` | Returns a random sample of n rows from the DataFrame. |
| Code Snippet | Explanation |
| --- | --- |
| `df.isnull().sum()` | Counts the number of missing values per column. |
| `df.duplicated().sum()` | Counts the number of duplicate rows in the DataFrame. |
| `df.describe()` | Provides basic descriptive statistics of numerical columns. |
| `df.info()` | Displays a concise summary of the DataFrame including data types and presence of null values. |
| `df.nunique()` | Counts the number of unique values per column. |
| `df.apply(lambda x: x.nunique() / x.count() * 100)` | Computes the percentage of unique values for each column. |
| `df.isin( value » ).sum()` | Counts the number of occurrences of a specific value across all columns. |
| `df.applymap(lambda x: isinstance(x, type_to_check)).sum()` | Counts the number of values of a specific type (e.g., int, str) per column. |
| `df.dtypes` | Lists the data type for each column in the DataFrame. |
| `df.sample(n)` | Returns a random sample of n rows from the DataFrame. |
Mastering specific Pandas functions can enhance data manipulation skills for data scientists using Python, focusing on less explored methods for data transformation and analysis.
PyStore is a simple (yet powerful) datastore for Pandas dataframes, designed with storing timeseries data in mind. It leverages Pandas, Numpy, Dask, and Parquet (via pyarrow) for efficient data handling.
Turn your Pandas data frame into a knowledge graph using LLMs. Learn how to build your own LLM graph-builder, implement LLMGraphTransformer by LangChain, and perform QA on your knowledge graph.
Reset a pandas DataFrame index
This article demonstrates how to use Pandas plotting capabilities for common data visualization tasks, suggesting that Pandas can be sufficient for routine EDA without relying on libraries like Matplotlib.
An exploration of the benefits of switching from the popular Python library Pandas to the newer Polars for data manipulation tasks, highlighting improvements in performance, concurrency, and ease of use.
There’s a reason you’re confused
