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Use pandas to load a table, inspect what actually came in, make deliberate fixes, and verify the result before exporting. The key is to choose each cleaning rule from the meaning of the data—not from a blanket rule that every blank, repeated value, or unusual type is wrong.
What pandas does with tabular data
pandas is an open-source Python library for working with labeled and relational data. It represents a table as a DataFrame and supports exploring, cleaning, processing, and writing data. As the pandas project puts it, “pandas will help you to explore, clean, and process your data.” See the pandas package overview and getting-started guide.
A safe beginner workflow is: read the source, inspect it before editing, decide what counts as a problem for this dataset, apply a justified change, and repeat the checks. A spreadsheet-shaped file is not automatically clean just because it opens successfully.
How do I read and write tabular data?
Use the reader and writer that match the file format. pandas supports common sources such as CSV, Excel, SQL, JSON, and Parquet; CSV is a useful starting example. The read-and-write tutorial explains the format-specific functions.
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import pandas as pd
df = pd.read_csv("sales.csv")pd.read_csv() reads a CSV into a DataFrame. A CSV reader infers column types by default, which is convenient but may not match the column’s intended meaning. You can supply dtype for explicit types and na_values for additional strings that should count as missing. Only add a marker such as "N/A" if it truly means a missing value in this file; it could be a legitimate value in another dataset.
df = pd.read_csv(
"sales.csv",
dtype={"customer_id": "string"},
na_values=["", "N/A"]
)Here, the ID is kept as text because digits in an identifier label a record rather than measure an amount. Choose the missing markers and types to fit the source, not merely to make the code run.
What should I inspect before cleaning?
Look at representative rows and the DataFrame’s structure before changing anything. This helps catch unexpected headers, values, inferred types, and missingness early.
print(df.head())
print(df.tail())
print(df.dtypes)
df.info()head()andtail()show sample rows at the beginning and end.dtypeslists the inferred or specified type for each column.info()reports dimensions, non-null counts, types, and an approximate memory footprint.
If a date appears as text or a numeric-looking field has missing entries, you have a clue to investigate—not proof that conversion or deletion is automatically right. First establish what one row represents, which columns identify a record, what values are valid, and whether blanks have consistent meaning.
How do I find and handle missing values in pandas?
Missing values can be detected, dropped, or filled, but pandas’ missing marker varies by dtype. Use pandas’ missing-data guidance rather than assuming all blanks are represented identically: Working with missing data.
Compare missingness by column with:
df.isna().sum()Then select a rule based on the column’s role and the analysis that follows. Dropping rows or columns removes information; filling keeps rows but introduces an assumption about what the absent value should mean.
| Approach | What it does | When it may fit | Main trade-off |
|---|---|---|---|
| Drop rows with missing values | Removes affected observations. | When the missing field is essential to the question and the loss of those rows is acceptable. | Fewer observations remain, and the removed rows may not be interchangeable with retained ones. |
| Drop a column | Removes a field with missing values. | When the field is not needed for the task and cannot be reliably used. | Potentially useful information is discarded. |
| Fill missing values | Replaces missing entries with a chosen value or method. | When a defensible replacement follows from the field’s meaning and intended use. | The replacement adds an assumption and can affect summaries or later analysis. |
For example, a blank quantity might mean “not recorded,” not zero. Filling it with zero changes the interpretation; dropping the row also changes which observations are included. Decide explicitly, apply the relevant pandas operation only after that decision, and check how many values or rows changed.
How do I check what data types pandas read?
Use df.dtypes and df.info() to compare imported types with the meaning of each field. Inferred types are convenient, while explicit dtype choices are more predictable; explicit choices still require validating the input because a requested conversion cannot make invalid source values meaningful. The pandas CSV tutorial and read_csv reference cover inference, dtype, and na_values.
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| Choice | Benefit | Check to make |
|---|---|---|
| Accept type inference | Less setup when the file is consistent and inferred types match the intended use. | Inspect the resulting dtypes and sample values; a date or number may have been read as text. |
| Specify a dtype | More predictable handling, especially for fields such as IDs that should remain text. | Confirm the source values fit the chosen type and that the type suits later operations. |
Do not turn every column of digits into a number: postal codes, account identifiers, and similar labels are often categorical text. Conversely, a numeric measurement imported as text may need conversion, but inspect and validate its values first.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I remove duplicate rows in pandas?
Decide what makes a record a duplicate before removing anything. Two rows identical in every column may be repeated imports, but two rows sharing an ID can still represent separate events. The DataFrame API provides duplicated() to identify matches and drop_duplicates() to remove them, including comparisons based on selected columns and a chosen keep behavior. See the duplicated reference and drop_duplicates reference.
| Duplicate rule | What is compared | Best suited to |
|---|---|---|
| Full-row match | All columns by default. | Finding rows that are exact repeats across the entire table. |
| Selected key columns | Only columns you specify. | Finding multiple rows for a record key, when your data rules say that key should be unique. |
For a table where order_id is supposed to identify one record, first inspect repeated keys:
repeated_orders = df[df.duplicated(subset=["order_id"], keep=False)]
print(repeated_orders)Review the matches and confirm the key’s uniqueness rule before using drop_duplicates(subset=["order_id"]). Removing repeats without defining the key can erase valid events or conflicting records.
How do I validate and export the cleaned DataFrame?
Repeat the same structural and missingness checks after each material change, then compare the outcome with what you expected. A lower row count is not itself evidence of success: explain why records changed and confirm that the result follows your cleaning rules.
print(df.shape)
print(df.dtypes)
print(df.isna().sum())
df.info()When the result is ready, write it using the format-specific to_* method that matches your output. For example, a CSV output can be written with to_csv(); use the corresponding writer for other supported formats. Keep the original source unchanged or separately available so that you can review what the cleaning steps did. pandas’ learning resources include tutorials, the user guide, a cheat sheet, and “10 Minutes to pandas” for the next step.
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