Selecting Rows, Missing Values and Duplicate Values

• loc[] is used to select rows and columns using labels.

print(df.loc[0])

print(df.loc[0, "Name"])

• iloc[] selects data using integer positions.

print(df.iloc[0])

print(df.iloc[0, 0])

• isin() checks whether values belong to a given list.

result = df[df["Name"].isin(["Arun", "Ravi"])]

print(result)

• between() checks whether values are within a range.

result = df[df["Marks"].between(70, 90)]

print(result)

Renaming Columns

df = df.rename(
    columns={"Marks": "Score"}
)
print(df)

Sorting Data

• Ascending

df = df.sort_values("Marks")

print(df)

• Descending

df = df.sort_values("Marks", ascending=False)

print(df)

Missing Values

NaN means the value is missing.

• Finding Missing Values

print(df.isnull())

print(df.isnull().sum())

• Removing Missing Values

df = df.dropna()

• Filling Missing Values

df["Marks"] = df["Marks"].fillna(0)

Duplicate Values

• Find duplicates

print(df.duplicated())

• Remove duplicates

df = df.drop_duplicates()