Pandas 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 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.
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