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 MoreBeginner's Guide to pandas Index: Mastering Data Sorting and Renaming Effortlessly
### Detailed Explanation of pandas Index An index is a key element in pandas for identifying data positions and content, similar to row numbers/column headers in Excel. It serves as the "ID card" of data, with core functions including quick data location, supporting sorting and merging operations. **Data Sorting**: - **Series Sorting**: To sort by index, use `sort_index()` (ascending by default; set `ascending=False` for descending order). To sort by values, use `sort_values()` (ascending by default; same parameter for descending order). - **DataFrame Sorting**: Sort by column values using `sort_values(by=column_name)`, and sort by row index using `sort_index()`. **Renaming Indexes**: - Modify row/column labels with `rename()`, e.g., `df.rename(index={old_name: new_name})` or `df.rename(columns={old_name: new_name})`. - Direct assignment: `df.index = [new_index]` or `df.columns = [new_column_names]`, with length consistency required. **Notes**: - Distinguish between row index (`df.index`) and column index (`df.columns`). - When modifying indexes,
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 Series: From Understanding to Practical Operations, Even Beginners Can Grasp It
A Series in pandas is a labeled one-dimensional array containing data and indices, serving as a fundamental data processing structure. It can be created in various ways: from a list (with default 0, 1... indices), a dictionary (with keys as indices), a scalar value with a specified length (resulting in repeated values), or with a custom index (e.g., dates, strings). Key attributes include values (the data array), index (the labels), name (the Series name), and shape (the dimensions). Indexing operations support label-based access (loc) and positional access (iloc). Notably, label-based slicing includes the end label, while positional slicing does not. Data operations include statistical methods like sum and mean, as well as filtering via boolean conditions. In practical applications, Series are used for time series or labeled data (e.g., passenger flow analysis), enabling quick positioning, statistics, and filtering through index manipulation. Mastering index operations is crucial for effective data processing.
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 MorePandas Tutorial for Beginners: Missing Value Handling from Entry to Practice
This article introduces methods for handling missing values in data analysis. Missing values refer to non-valid values in a dataset, represented as `NaN` in pandas. Before processing, it is necessary to first check: `isnull()` to mark missing values, `isnull().sum()` to count the number of missing values in each column, and `info()` to view the overall distribution of missing values. Processing strategies are divided into deletion and imputation: Deletion uses `dropna()`, which deletes records containing missing values by row (default) or by column; Imputation uses `fillna()`, including fixed values (e.g., 0), statistical measures (mean/median for numerical values, mode for categorical values), and forward/backward filling (`ffill/bfill`, suitable for time series). Taking e-commerce order data as an example, the case first checks for missing values, then uses the mean to impute the "amount" column and the mode to impute the "payment method" column. The core steps of processing are: check for missing values → select a strategy (delete for extremely few values, impute for many values or key data) → verify the result. It is necessary to flexibly choose methods based on the characteristics of the data.
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 MoreLearning pandas from Scratch: A Step-by-Step Guide to Reading CSV Files
This article introduces the introductory steps to learning pandas for data processing, with the core being reading CSV files and performing basic data operations. First, pandas is likened to the "steward" of data processing, and reading CSV is the first step in data analysis. The steps include: installing pandas (using `pip install`, or skipping if pre-installed with Anaconda/Jupyter); importing pandas as `import pandas as pd`; reading the CSV file with `pd.read_csv()` to generate a DataFrame; viewing data using `head()`/`tail()` for preview, `info()` to check data types and missing values, and `describe()` for numerical statistics; handling special formats such as Chinese garbled characters (via `encoding`), delimiters (via `sep`), and no header rows (via `names`). The article concludes by summarizing the basic skills acquired, noting that this is just the beginning of data processing, and subsequent advanced operations like filtering and cleaning can be learned next.
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