Q&A 18 How do you inspect variable types in a dataset?
18.1 Explanation
Inspecting variable types is one of the first steps in understanding the structure of your dataset. It helps you:
- Know whether variables are categorical, numeric, text, or dates
- Identify potential conversion needs (e.g., strings to datetime)
- Choose appropriate analysis techniques and visualization methods
Most tools provide a built-in way to inspect data types for each column.
18.2 Python Code
import pandas as pd
# Load a sample dataset
df = pd.DataFrame({
"name": ["Alice", "Bob"],
"age": [30, 25],
"member": [True, False],
"joined": pd.to_datetime(["2022-01-01", "2021-07-15"])
})
# Inspect data types of each column
print(df.dtypes)
# Optional: check class of an individual column
print(type(df["age"]))name object
age int64
member bool
joined datetime64[ns]
dtype: object
<class 'pandas.core.series.Series'>18.3 R Code
# Create a sample data frame
df <- data.frame(
name = c("Alice", "Bob"),
age = c(30, 25),
member = c(TRUE, FALSE),
joined = as.Date(c("2022-01-01", "2021-07-15"))
)
# Inspect structure of the dataset
str(df)'data.frame': 2 obs. of 4 variables:
$ name : chr "Alice" "Bob"
$ age : num 30 25
$ member: logi TRUE FALSE
$ joined: Date, format: "2022-01-01" "2021-07-15"[1] "numeric"