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You can automate descriptive summaries in Python with pandas, but DataFrame.describe() is a starting point—not a complete analysis plan. Choose the columns and statistics that answer your question, check missing values and data types, then save the code and assumptions so you can rerun the summary when your data changes.
1. Decide what the summary needs to answer
Start with the question, not the function. A summary for numeric measurements might need a mean and quartiles; a summary of categories may need the most common value; a time column may need a date range. List the columns and useful statistics before writing code.
- Numeric: count, mean, spread, minimum, maximum, and selected percentiles.
- Categorical: number of observed values, number of distinct categories, most frequent category, and its frequency.
- Temporal: the earliest and latest dates may be more useful than a numeric mean. Confirm that the column is parsed as dates before summarizing it.
This choice matters because the output depends on the input column types and the options you request.
2. Load the tabular data and inspect it
For a CSV file, use pandas’ read_csv() to create a DataFrame. The pandas tutorial demonstrates this CSV-to-DataFrame workflow in its summary statistics tutorial.
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import pandas as pd
df = pd.read_csv("data.csv")
print(df.head())
print(df.dtypes)head() gives a quick look at sample rows, while dtypes shows how pandas interpreted each column. A number stored as text, for example, will not be included in the default numeric summary as a numeric column. If a date column was read as text, parse it deliberately when loading or convert it before choosing a date summary.
3. Check missing values before interpreting the output
Pandas descriptive summaries exclude missing values. As a result, count can differ across columns: each statistic may be based on a different number of non-missing observations. The pandas API describes descriptive statistics as summarizing central tendency, dispersion, and distribution shape while excluding NaN values (DataFrame.describe documentation).
print(df.isna().sum())Compare these missing-value counts with the counts in your summary. If columns have different counts, decide whether those differences are acceptable for the question; do not assume every reported mean or percentile uses the same rows. This workflow leaves missing values in place and makes their effect visible rather than silently imputing or dropping them.
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4. Generate a baseline with describe()
Call describe() on the DataFrame to get a quick baseline for numeric columns:
summary = df.describe()
print(summary)For numeric columns, the default result contains count, mean, standard deviation, minimum, maximum, and the 25th, 50th, and 75th percentiles. The 50th percentile is the median. The default quartiles help show the distribution’s middle half, but they do not replace statistics chosen for a specific analysis.
To summarize only selected columns, select them first:
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columns = ["age", "fare"]
summary = df[columns].describe()
print(summary)Use the actual column names from your file. This pattern is useful when a dataset has many fields but your report concerns only a few measurements.
5. Include categorical or other column types when they matter
By default, a DataFrame summary focuses on numeric columns. To request summaries for every column, use include="all"; use include or exclude to target types as appropriate for your DataFrame.
all_summary = df.describe(include="all")
print(all_summary)Categorical summaries use different measures from numeric summaries: count, unique-value count, the most frequent value (top), and its frequency (freq). A mean or quartile is not meaningful for a category such as a product name or region, so pandas does not report numeric measures for it.
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Inspect the resulting columns and rows rather than assuming one uniform set of statistics applies to every type. The available output reflects the columns and types pandas sees.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Customize percentiles and per-column statistics
If the default quartiles do not match your reporting needs, pass the percentile values you want. For example, this requests the 10th, 50th, and 90th percentiles for two numeric columns:
summary = df[["age", "fare"]].describe(percentiles=[0.10, 0.50, 0.90])
print(summary)For a more tailored table, use DataFrame.agg(). It accepts different functions for different columns, which is useful when one field needs a mean while another needs a minimum and maximum. The pandas tutorial shows this per-column aggregation approach (How to calculate summary statistics).
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custom = df.agg({
"age": ["count", "mean", "median"],
"fare": ["min", "median", "max"]
})
print(custom)Replace the example names and functions with those relevant to your columns. A custom aggregation makes the requested measures explicit instead of returning every default measure whether or not you need it.
7. Choose the statistical function that matches the request and keep it repeatable
Use pandas describe() or agg() for convenient DataFrame summaries, particularly when you need to select and summarize different column types. If the requested output specifically includes observation count, variance, skewness, or kurtosis, SciPy’s stats.describe may fit better. Its documented results include count, minimum and maximum, mean, variance, skewness, and kurtosis (SciPy stats.describe reference).
from scipy import stats
result = stats.describe(values, nan_policy="omit")
print(result)Here, values should be the numeric observations you intend to analyze, such as a selected pandas Series. SciPy documents three nan_policy choices: propagate (the default), raise, and omit. Choose deliberately: omit excludes NaNs, while raise fails if they are present; with the default, NaNs propagate into the result. Check the SciPy reference for the exact behavior of the installed version and the return values you need.
These functions are not interchangeable for every input. pandas provides DataFrame-oriented summaries and per-column aggregation; SciPy is an option when its particular statistical measures and NaN policy are required. The Python standard-library statistics module is documented as a basic numeric-statistics library, not a replacement for full-featured third-party packages (Python statistics documentation).
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import pandas as pd
df = pd.read_csv("data.csv")
columns = ["age", "fare"]
summary = df[columns].describe(percentiles=[0.10, 0.50, 0.90])
print(summary)When the file changes, rerunning the same code applies the same selection and summary rules to the new data. Recheck the column types and missing-value counts as part of that run, since changes in the input can change what the output means.
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