["python","pandas","dataframe","indexing","filter"]
How do I select rows from a DataFrame based on column values?

How can I select rows from a DataFrame based on values in some column in Pandas? In SQL, I would use: SELECT * FROM table WHERE column_name = some_value
To select rows whose column value equals a scalar, some_value, use ==: df.loc[df['column_name'] == some_value] To select rows whose column value is in an iterable, some_values, use isin: df.loc[df['column_name'].isin(some_values)] Combine multiple conditions with &: df.loc[(df['column_name'] >= A) & (df['column_name'] <= B)] Note the parentheses. Due to Python's operator precedence rules, & binds more tightly than <= and >=. Thus, the parentheses in the last example are necessary. Without the parentheses df['column_name'] >= A & df['column_name'] <= B is parsed as df['column_name'] >= (A & df['column_name']) <= B which results in a Truth value of a Series is ambiguous error. To select rows whose column value does not equal some_value, use !=: df.loc[df['column_name'] != some_value] The isin returns a boolean Series, so to select rows whose value is not in some_values, negate the boolean Series using ~: df = df.loc[~df['column_name'].isin(some_values)] # .loc is not in-place replacement For example, import pandas as pd import numpy as np df = pd.DataFrame({'A': 'foo bar foo bar foo bar foo foo'.split(), 'B': 'one one two three two two one three'.split(), 'C': np.arange(8), 'D': np.arange(8) * 2}) print(df) # A B C D # 0 foo one 0 0 # 1 bar one 1 2 # 2 foo two 2 4 # 3 bar three 3 6 # 4 foo two 4 8 # 5 bar two 5 10 # 6 foo one 6 12 # 7 foo three 7 14 print(df.loc[df['A'] == 'foo']) yields A B C D 0 foo one 0 0 2 foo two 2 4 4 foo two 4 8 6 foo one 6 12 7 foo three 7 14 If you have multiple values you want to include, put them in a list (or more generally, any iterable) and use isin: print(df.loc[df['B'].isin(['one','three'])]) yields A B C D 0 foo one 0 0 1 bar one 1 2 3 bar three 3 6 6 foo one 6 12 7 foo three 7 14 Note, however, that if you wish to do this many times, it is more efficient to make an index first, and then use df.loc: df = df.set_index(['B']) print(df.loc['one']) yields A C D B one foo 0 0 one bar 1 2 one foo 6 12 or, to include multiple values from the index use df.index.isin: df.loc[df.index.isin(['one','two'])] yields A C D B one foo 0 0 one bar 1 2 two foo 2 4 two foo 4 8 two bar 5 10 one foo 6 12