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 Super Useful Tips: Getting Started with Data Cleaning, Easy for Beginners to Master
Data cleaning is crucial for data analysis, and pandas is an efficient tool for this task. This article teaches beginners how to perform core data cleaning using pandas: first, install and import data (via `pd.read_csv()` or creating a sample DataFrame), then use `head()` and `info()` for initial inspection. For missing values: identify with `isnull()`, remove with `dropna()`, or fill with `fillna()` (e.g., mean/median). Duplicates are detected via `duplicated()` and removed with `drop_duplicates()`. Outliers can be identified through `describe()` statistics or logical filtering (e.g., income ≤ 20000). Data type conversion is done using `astype()` or `to_datetime()`. The beginner workflow is: Import → Inspect → Handle missing values → Duplicates → Outliers → Type conversion. Emphasize hands-on practice to flexibly apply these tools to solve real-world data problems.
Read MorePandas Data Merging: Basic Operations of merge and concat, Suitable for Beginners
This article introduces two data merging tools in pandas: `merge` and `concat`, suitable for beginners to quickly master. **concat**: No associated keys, direct concatenation, either row-wise (axis=0) or column-wise (axis=1). Row concatenation (axis=0) is suitable for tables with the same structure (e.g., multi-month data), and it is important to use `ignore_index=True` to reset the index and avoid duplicates. Column concatenation (axis=1) requires the number of rows to be consistent, used for merging by row identifiers (e.g., student information + grade table). **merge**: Merging based on common keys (e.g., name, ID), similar to SQL JOIN, supporting four methods: `inner` (default, retains common keys), `left` (retains left table), `right` (retains right table), and `outer` (retains all). When key names differ, use `left_on`/`right_on` to specify. The default merging method is `inner`. **Key Difference**: concat concatenates without keys, while merge matches by keys. Beginners should note: for column-wise concat, the number of rows must be consistent; merge uses the `how` parameter to control the merge scope, and avoid index duplication and key name mismatch issues.
Read MoreMust - read for Beginners! Basic Operations in pandas: Creating, Viewing, and Modifying Data
This article introduces basic pandas operations, covering data creation, viewing, and modification. **Data Creation**: The core structures are Series (1D with index) and DataFrame (2D table). A Series can be created from a list (with default 0,1… indices) or custom indices (e.g., ['a','b']). A DataFrame can be created from a dictionary (keys = column names, values = column data) or a 2D list (with columns specified explicitly). **Data Viewing**: `head(n)`/`tail(n)` previews the first/last n rows (default 5 rows). `info()` shows data types and non-null values; `describe()` summarizes numerical columns (count, mean, etc.). `columns`/`index` display column names and row indices, respectively. **Data Modification**: Cell values are modified using `loc[label, column]` (label-based) or `iloc[position, column position]` (position-based). New columns are added via direct assignment (e.g., `df['Class'] = 'Class 1'`) or calculations based on existing columns. Columns are dropped with `drop(column name, axis=1, inplace=True)`. Indices can be modified by direct assignment to `index`/`columns` or renamed using `rename()`. The core is "locating data," requiring clear distinction between `loc` (label-based) and `iloc` (position-based) indexing.
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.
Read MoreNumpy Array Reshaping: A Beginner's Guide to reshape and flatten
This article introduces two practical methods for array reshaping in Numpy: `reshape` and `flatten`, which are used to meet different data processing needs. The core premise is that the total number of array elements before and after reshaping must be consistent. The `reshape` method can change the array shape (e.g., 1D to 2D). Its syntax is `arr.reshape(new_shape)`, which supports specifying the shape with a tuple. Using `-1` allows automatic calculation of the missing dimension (e.g., if the number of rows is 3, the number of columns is automatically calculated). It returns a new array without modifying the original array. The `flatten` method flattens a multi-dimensional array into a 1D array and returns a new array (a copy), avoiding modification of the original array. Unlike `ravel` (which returns a view), `flatten` is recommended for priority use. A common error is "mismatched element count", where it is necessary to ensure that the product of the `reshape` parameters equals the size of the original array (`original_array.size`). In summary, `reshape` flexibly adjusts the shape, and `flatten` safely flattens to 1D. Mastering both methods enables efficient array reshaping and lays the foundation for data processing (e.g., in machine learning).
Read MoreNumpy Statistical Analysis: Quick Start with mean, sum, and max Functions
This article introduces the usage methods of three commonly used statistical functions in NumPy: `mean` (average), `sum` (summation), and `max` (maximum). As a core tool for Python data analysis, NumPy provides efficient multidimensional arrays and statistical functions. All three functions support the `axis` parameter to control the calculation direction: `axis=0` calculates column-wise (vertically), `axis=1` calculates row-wise (horizontally), and if not specified, the overall value is computed. - **mean**: Computes the arithmetic mean of array elements. For a one-dimensional array, it returns the overall average; for a two-dimensional array, it can compute column-wise or row-wise averages. - **sum**: Computes the sum of array elements. Similar to `mean`, it specifies row or column summation via the `axis` parameter. - **max**: Finds the maximum value in the array, also supporting maximum value calculation across rows or columns. The article demonstrates basic usage with one-dimensional and two-dimensional array examples, and applies them to a practical case of student scores (3 students × 3 courses): calculating the average score per course, total score per student, and highest score. This verifies the practicality of the functions. It concludes that mastering these three functions and the `axis` parameter is fundamental for data analysis, laying the groundwork for subsequent complex analyses.
Read MoreComprehensive Guide to Numpy Arrays: shape, Indexing, and Slicing
NumPy arrays are the foundation of Python data analysis, providing efficient multi-dimensional array objects with core operations including array creation, shape manipulation, indexing, and slicing. Creation methods: np.array() is commonly used to generate arrays from lists; zeros/ones create arrays filled with 0s/1s; arange generates sequences similar to Python's range. Shape is the dimension identifier of an array, viewed via .shape. The reshape() method adjusts dimensions (total elements must remain unchanged), with -1 indicating automatic dimension calculation. Indexing: 1D arrays behave like lists (0-based indexing with support for negative indices); 2D arrays use double indexing [i, j]. Slicing: Follows the syntax [start:end:step], with 1D/2D slicing producing subarrays. Slices return views by default (modifications affect the original array), requiring .copy() for independent copies. Mastering shape, indexing, and slicing is essential. Practical exercises are recommended to solidify these fundamental operations.
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