How do I select rows from a DataFrame based on column values?

Question

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 

I tried to look at Pandas' documentation, but I did not immediately find the answer.

Answer 1

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] 

isin returns a boolean Series, so to select rows whose value is not in some_values, negate the boolean Series using ~:

df.loc[~df['column_name'].isin(some_values)] 

---

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