Selecting, Filtering and Sorting

There are two label-aware selectors. .loc selects by label (column names, index values); .iloc selects by integer position.

import pandas as pd
df = pd.DataFrame({
    "name": ["Ada", "Bo", "Cy"],
    "age":  [30, 25, 35],
})
print(df.loc[0, "name"])   # Ada    (row label 0, column 'name')
print(df.iloc[0])          # the first row by position
print(df.iloc[:, 1])       # the second column by position

The workhorse is the boolean mask. Write a condition on a column to get a True/False Series, then index the DataFrame with it to keep only the True rows:

mask = df["age"] >= 30
print(df[mask])            # only Ada and Cy
# or in one line:
print(df[df["age"] >= 30])

Combine conditions with & (and) and | (or). Wrap each condition in parentheses:

print(df[(df["age"] >= 26) & (df["age"] <= 34)])   # just Ada

Sort with sort_values. Use ascending=False for largest first:

print(df.sort_values("age", ascending=False))   # Cy, Ada, Bo