Descriptive Statistics and Value Counts

After cleaning and preparing your DataFrame, the next step is to explore the distribution and summary of your data.

Pandas offers straightforward yet powerful tools for generating quick statistical overviews — helping you identify trends, anomalies, and insights with ease.

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 tool for initial data exploration and profiling.

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

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

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

df["Category"].value_counts()

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

You can apply these methods to individual columns or across the entire DataFrame to gain a deeper statistical understanding of your dataset.