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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallUse this quick reference to install pandas, create or load a table, inspect and select data, handle missing values, summarize columns, and move on to grouping, merging, and reshaping. The examples follow the pandas 3.0.6 documentation dated September 17, 2026; pandas is an open-source Python library for working with data structures and analysis. Official pandas documentation.
What is pandas, and what kind of data does it handle?
pandas is a Python library for exploring, cleaning, and processing tabular data, such as information kept in spreadsheets or databases. Its two core structures are Series and DataFrame.
- Series: a one-dimensional labeled array, like one column of values with an index.
- DataFrame: a two-dimensional labeled table. Its columns can hold different data types.
Labels are important: pandas aligns data by index and column labels during many operations. Two values that look like they are in the same position may not be combined if their labels differ. Learn the basics in the official introduction to pandas data structures.
How do I install and import pandas?
The pandas installation guide recommends installing and running pandas in a virtual environment. Choose the command that matches your package manager; the official docs also describe source installation for users who specifically need it.
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- Conda:
conda install -c conda-forge pandas - pip:
pip install pandas
Installation methods are alternatives, not a performance ranking. Check the pandas installation guide for current details.
In a Python script or notebook, use the conventional alias:
import pandas as pdHow do I create or read a table?
Create a small DataFrame
A dictionary is a convenient way to build a table: dictionary keys become column names, and corresponding lists supply the values.
import pandas as pd
data = {
"name": ["Ava", "Noah", "Mia"],
"score": [88, 92, 85],
}
df = pd.DataFrame(data)Read a CSV file
Use read_csv to load CSV data into a DataFrame. Replace the filename with your file’s path.
df = pd.read_csv("scores.csv")The reader functions generally follow a read_* naming pattern. pandas documentation lists CSV, Excel, SQL, JSON, and Parquet among supported data sources and formats. See the official read-and-write tutorial for format-specific examples.
How do I inspect a DataFrame?
Start by checking a few rows, the dimensions, column types, and a statistical summary. These quick checks help reveal whether the data loaded as expected.
df.head() # first rows
df.shape # (number of rows, number of columns)
df.columns # column labels
df.dtypes # data type of each column
df.info() # concise structural summary
df.describe() # summary statistics for numeric columnsFor example, df.shape returns a pair of counts, while df.describe() summarizes numeric columns. Consult the pandas basics guide for additional inspection and summary methods.
How do I select rows and columns?
Choose selection methods according to whether you know labels or integer positions. loc works with labels; iloc works with positions. Use at and iat for optimized access to a single labeled or position-based value, respectively. The 10 Minutes to pandas guide also introduces bracket selection, while recommending these access methods for production code.
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df["score"] # one column as a Series
df[["name", "score"]] # selected columns as a DataFrame
df.loc[0, "name"] # value at row label 0, column "name"
df.iloc[0, 1] # value at row position 0, column position 1
df.loc[df["score"] >= 90, ["name", "score"]] # rows meeting a conditionIn loc, the row key is an index label, which need not be the row’s current numeric position. For more detail, use the indexing and selecting data guide.
How do I handle missing data?
Check for missing values before deciding how to handle them. isna() marks missing entries, and sum() counts them by column. Depending on the task, you can drop rows with missing values or fill them with a chosen value.
df.isna().sum() # missing-value count per column
df.dropna() # return rows with no missing values
df.fillna(0) # return a copy with missing values replaced by 0Dropping or filling values changes what the data means, so choose a rule that fits the analysis rather than applying one automatically. The missing data guide covers the available options.
How do I transform columns and calculate summary statistics?
Many everyday changes can be expressed as operations on a column. Assigning the result back to a column stores the transformed values in the DataFrame.
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df["score_plus_one"] = df["score"] + 1
df["name_upper"] = df["name"].str.upper()
df["score"].mean() # mean of the score column
df["score"].min() # smallest score
df["score"].max() # largest scoreFor more summary statistics, try df["score"].median() or df["score"].value_counts(), depending on whether you need a numeric summary or a count of distinct values. pandas also supports elementwise column manipulation and descriptive statistics; see the basics guide.
When should I group, merge, or reshape data?
Group rows to summarize categories
Use groupby when you want a summary for each category, such as the average score for each class.
df.groupby("class")["score"].mean()Merge related tables
Use merge to combine DataFrames using a shared key, much like a database join.
result = pd.merge(students, grades, on="student_id")Reshape a table
Reshaping changes how values are arranged across rows and columns. For example, pivot can turn category values into columns when the chosen index-and-column combination identifies a single value.
wide = df.pivot(index="date", columns="category", values="amount")See the official guide sections on grouping, merging, and reshaping for options and details.
How do I write a table to a file?
Use the corresponding writer method for the format you need. For a CSV, call to_csv; index=False prevents the DataFrame index from being written as an extra column when it is not needed.
df.to_csv("cleaned_scores.csv", index=False)The read-and-write tutorial demonstrates supported formats and their matching methods. Confirm the options for your target format in the official tutorial.
What should I learn next?
If you are new to pandas, the project recommends starting with 10 Minutes to pandas. It introduces the basic structures and object creation, then covers viewing and selection, missing data, operations, merging, grouping, reshaping, time series, categoricals, plotting, and input/output. It is an overview, not a complete reference; follow the relevant links into the User Guide when you need the details for a specific task.
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