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Practice

Descriptive Statistics and Value Counts

Once you've cleaned and prepared your DataFrame, the next step is understanding the distribution and summary of your data.

Pandas provides simple and powerful tools to generate statistical overviews, which help you spot patterns, errors, or insights at a glance.


Descriptive Methods​

Use .describe() to get a quick statistical summary of all numeric columns:

  • Count of non-null values
  • Mean and standard deviation
  • Minimum and maximum values
  • 25%, 50%, and 75% percentiles

This method is your go-to for initial data profiling.


Categorical Analysis with value_counts()​

To summarize non-numeric (categorical) columns, use .value_counts().

It returns the frequency of each unique value in a column.

value_counts() example
df = pd.DataFrame({
    "Category": ["A", "A", "B", "B", "C", "C"]
})

df["Category"].value_counts()

# Output:
# B    2
# A    2
# C    2

Common Additional Methods​

MethodPurpose
mean()Average value
median()Middle value
std()Standard deviation
min() / max()Minimum and maximum values
sum()Total sum of column
count()Number of non-null entries

You can apply these methods either column-wise or across the entire DataFrame.

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