Indexing and Selecting Data with Pandas

Selecting a Single Column

first = data["Age"]

Selecting Multiple Columns

first = data[["Age", "College", "Salary"]]

Selecting a Single Row by Label

row = data.loc["Avery Bradley"]

Selecting Multiple Rows

rows = data.loc[["Avery Bradley", "R.J. Hunter"]]

Selecting Specific Rows and Columns

selection = data.loc[["Avery Bradley", "R.J. Hunter"], ["Team", "Number", "Position"]]

Selecting All Rows and Specific Columns

all_rows_specific_columns = data.loc[:, ["Team", "Position", "Salary"]]

Selecting a Single Row by Position

row = data.iloc[3]

Selecting Multiple Rows by Position

rows = data.iloc[[3, 5, 7]]

Selecting Specific Rows and Columns by Position

selection = data.iloc[[3, 4], [1, 2]]

Selecting All Rows and Specific Columns by Position

selection = data.iloc[:, [1, 2]]

.head(): Returns the first n rows of a DataFrame

print(data.head(5))

.tail(): Returns the last n rows of a DataFrame

print(data.tail(5))

.at[]: Access a single value for a row/column label pair

value = data.at["Avery Bradley", "Age"]

.query(): Query the DataFrame using a boolean expression

result = data.query("Age > 25 and College == 'Duke'")

DataFrame.iat[]

Access a single value for a row/column pair by integer position.

DataFrame.pop()

Return item and drop from DataFrame.

DataFrame.xs()

Return a cross-section (row(s) or column(s)) from the DataFrame.

DataFrame.get()

Get item from object for given key (e.g DataFrame column).

DataFrame.isin()

Return a boolean DataFrame showing whether each element is contained in values.

DataFrame.where()

Return an object of the same shape with entries from self where cond is True otherwise from other.

DataFrame.mask()

Return an object of the same shape with entries from self where cond is False otherwise from other.

DataFrame.insert()

Insert a column into DataFrame at a specified location.