bigframes.pandas.DataFrame.query#

DataFrame.query(expr:str)→DataFrame[source]#

Query the columns of a DataFrame with a boolean expression.

Examples:

>>>df=bpd.DataFrame({'A':range(1,6),...'B':range(10,0,-2),...'C C':range(10,5,-1)})>>>dfA   B  C C0  1  10   101  2   8    92  3   6    83  4   4    74  5   2    6[5 rows x 3 columns]>>>df.query('A > B')A  B  C C4  5  2    6[1 rows x 3 columns]

The previous expression is equivalent to

>>>df[df.A>df.B]A  B  C C4  5  2    6[1 rows x 3 columns]

For columns with spaces in their name, you can use backtick quoting.

>>>df.query('B == `C C`')A   B  C C0  1  10   10[1 rows x 3 columns]

The previous expression is equivalent to

>>>df[df.B==df['C C']]A   B  C C0  1  10   10[1 rows x 3 columns]

Parameters:

expr (str) –

The query string to evaluate.

You can refer to variablesin the environment by prefixing them with an ‘@’ character like@a+b.

You can refer to column names that are not valid Python variable namesby surrounding them in backticks. Thus, column names containing spacesor punctuations (besides underscores) or starting with digits must besurrounded by backticks. (For example, a column named “Area (cm^2)” wouldbe referenced as`Area(cm^2)`). Column names which are Python keywords(like “list”, “for”, “import”, etc) cannot be used.

For example, if one of your columns is calledaa and you wantto sum it withb, your query should be`aa`+b.

Returns:

DataFrame result after the query operation, otherwise None.

Return type:

None orbigframes.pandas.DataFrame