pandas Sorting Operations: An Introduction and Practical Guide to the sort_values Function
This article introduces the sorting method of the `sort_values` function in pandas, which is applicable to sorting DataFrame/Series data. Core parameters: `by` specifies the column(s) to sort by (required), `ascending` controls ascending/descending order (default is ascending True), and `inplace` determines whether to modify the original data (default is False, returning a new dataset). Basic usage: Single-column sorting, e.g., ascending order by "Chinese" (default) or descending order by "Math"; multi-column sorting can pass a list of column names and corresponding ascending/descending directions (e.g., first by "Chinese" ascending, then by "Math" descending). Setting `inplace=True` directly modifies the original data; it is recommended to prioritize preserving the original data (default False). Practical examples: After adding a "Total Score" column, sort by total score in descending order to clearly display the ranking of comprehensive scores. Notes: For multi-column sorting, ensure the lengths of the `by` and `ascending` lists are consistent; prioritize data safety to avoid accidental overwriting of original data. By mastering core parameters and common scenarios through examples, sorting serves as a foundational step in data processing, becoming more critical when combined with subsequent analyses (e.g., TopN).
Read MorePandas Data Statistics: 5 Common Functions to Quickly Master Basic Analysis
Pandas is a powerful tool for processing tabular data in Python. This article introduces 5 basic statistical functions to help beginners quickly master data analysis skills. - **sum()**: Calculates the total sum, automatically ignoring missing values (NaN). Using `axis=1` allows summation by rows, which is useful for total statistics (e.g., total scores). - **mean()**: Computes the average, reflecting central tendency, but is sensitive to extreme values. Suitable for scenarios without extreme values. - **median()**: Calculates the median, which is robust to extreme values and better reflects the "true level of most data." - **max()/min()**: Returns the maximum/minimum values, respectively, for statistical extremes (e.g., highest/lowest scores). - **describe()**: Provides a one-stop statistical summary, outputting count, mean, standard deviation, quantiles, etc., to comprehensively understand data distribution and variability. These functions address basic questions like "total amount, average, middle level, and extreme values," serving as the "basic skills" of data analysis. Subsequent learning can advance to skills like groupby for more advanced statistics.
Read MoreIntroduction to pandas DataFrame: 3-Step Quick Start for Data Selection and Filtering
This article introduces 3 core steps for data selection and filtering in pandas DataFrames, suitable for beginners to quickly master. Step 1: Column Selection. For a single column, use `df['column_name']` to return a Series; for multiple columns, use `df[['column_name1', 'column_name2']]` to return a DataFrame. Step 2: Row Selection. Two methods are provided: `iloc` (by position, integer indexing) and `loc` (by label, custom index). Examples: `df.iloc[row_range]` or `df.loc[row_label]`. Step 3: Conditional Filtering. For single conditions, use `df[condition]`. For multiple conditions, connect them with `&` (AND) / `|` (OR), and each condition must be enclosed in parentheses. Key Reminder: When filtering with multiple conditions, always use `&`/`|` instead of `and`/`or`, and enclose each condition in parentheses. Through these three steps, basic data extraction can be completed, laying the foundation for subsequent analysis.
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