Pandas 根据布尔条件选择行和列

Question

I have a pandas dataframe with about 50 columns and >100 rows. I want to select columns 'col_x' , 'col_y' where 'col_z' < m . Is there a simple way to do this, similar to df[df['col3'] < m] and df[['colx','coly']] but combined?

Answer 1

Let's break down your problem. You want to

1. Filter rows based on some boolean condition
2. You want to select a subset of columns from the result.

For the first point, the condition you'd need is -

df["col_z"] < m

For the second requirement, you'd want to specify the list of columns that you need -

["col_x", "col_y"]

How would you combine these two to produce an expected output with pandas? The most straightforward way is using loc -

df.loc[df["col_z"] < m, ["col_x", "col_y"]]

The first argument selects rows, and the second argument selects columns.

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More About loc

Think of this in terms of the relational algebra operations - selection and projection . If you're from the SQL world, this would be a relatable equivalent. The above operation, in SQL syntax, would look like this -

SELECT col_x, col_y     # projection on columns
FROM df
WHERE col_z < m         # selection on rows

pandas loc allows you to specify index labels for selecting rows. For example, if you have a dataframe -

   col_x  col_y
a      1      4
b      2      5
c      3      6

To select index a , and c , and col_x you'd use -

df.loc[['a', 'c'], ['col_x']]

   col_x
a      1
c      3

Alternatively, for selecting by a boolean condition (using a series/array of bool values, as your original question asks), where all values in col_x are odd -

df.loc[(df.col_x % 2).ne(0), ['col_y']]

   col_y
a      4
c      6

For details, df.col_x % 2 computes the modulus of each value with respect to 2 . The ne(0) will then compare the value to 0 , and return True if it isn't (all odd numbers are selected like this). Here's what that expression results in -

(df.col_x % 2).ne(0)

a     True
b    False
c     True
Name: col_x, dtype: bool

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Further Reading

• 10 Minutes to Pandas - Selection by Label
• Indexing and selecting data
  • Boolean indexing
• Selection with .loc in python
• pandas loc vs. iloc vs. ix vs. at vs. iat?