Pandas 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 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.
Read More