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.
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.
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