Selecting Columns
Access one or multiple columns from a DataFrame
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Explanation
Single column — returns a Series (1D): ``python df['age'] # bracket notation (always works) df.age # dot notation (only works if no spaces in name)
Multiple columns — returns a DataFrame (2D): ``python df[['name', 'age']] # double brackets — list of column names
The difference matters:
python type(df['age']) # pandas.Series type(df[['age']]) # pandas.DataFrame
Series is a single column with an index. It has methods like: - s.mean(), s.max(), s.value_counts() - s.unique() — unique values - s.nunique() — count of unique values
Getting column names:
python df.columns # Index of all column names df.columns.tolist() # as a plain Python list
Examples
Series vs DataFrame
Single bracket = Series, double bracket = DataFrame
import pandas as pd
df = pd.DataFrame({'name':['Alice','Bob','Carol'],'age':[25,30,35],'salary':[50000,65000,80000]})
ages = df['age'] # Series
print(type(ages)) # <class 'pandas.core.series.Series'>
print(ages.mean()) # 30.0
subset = df[['name','age']] # DataFrame
print(type(subset)) # <class 'pandas.core.frame.DataFrame'>Next in pandas
loc vs iloc