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Cheatsheet 1

In pandas, there are a few quick + reliable ways to check the data type of columns, depending on what you need.

1️⃣ Check all column types (most common)


df.dtypes

Output example

age int64 height float64 name object active bool date datetime64[ns] dtype: object

2️⃣ Detailed overview (very useful for debugging)


df.info()

Shows:

  • column name

  • non-null count

  • dtype

  • memory usage

3️⃣ Check type of a single column


df["age"].dtype

or


df.age.dtype

4️⃣ Check only numeric / non-numeric columns


df.select_dtypes(include="number").columns

df.select_dtypes(exclude="number").columns

5️⃣ Check if a column is a specific type


import pandas as pd pd.api.types.is_numeric_dtype(df["age"]) pd.api.types.is_string_dtype(df["name"]) pd.api.types.is_datetime64_any_dtype(df["date"]) pd.api.types.is_bool_dtype(df["active"])

6️⃣ Count how many columns per dtype


df.dtypes.value_counts()

7️⃣ Detect mixed or problematic types (object columns)


df.select_dtypes(include="object").apply( lambda s: s.map(type).value_counts() )

Useful when:

  • CSV imported badly

  • numbers stored as strings

  • mixed types in one column

 

Task Command
See all dtypes df.dtypes
Full summary df.info()
Single column df[col].dtype
Only numeric select_dtypes()
Type check

pd.api.types.is_*

 
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