Pandas 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.