Pandas query function not working with spaces in column names

Question

I have a dataframe with spaces in column names. I am trying to use query method to get the results. It is working fine with 'c' column but getting error for 'a b'

import pandas as pd
a = pd.DataFrame(columns=["a b", "c"])
a["a b"] = [1,2,3,4]
a["c"] = [5,6,7,8]
a.query('a b==5')

For this I am getting this error:

a b ==5
  ^
SyntaxError: invalid syntax

I don't want to fill up space with other characters like '_' etc.

There is one hack using pandasql to put variable name inside brackets example: [a b]

Answer 1

From pandas 0.25 onward you will be able to escape column names with backticks so you can do

a.query('`a b` == 5') 

Answer 2

Pandas 0.25+

As described here:

DataFrame.query() and DataFrame.eval() now supports quoting column names with backticks to refer to names with spaces (GH6508)

So you can use:

a.query('`a b`==5')

Pandas pre-0.25

You cannot use pd.DataFrame.query if you have whitespace in your column name. Consider what would happen if you had columns named a, b and a b; there would be ambiguity as to what you require.

Instead, you can use pd.DataFrame.loc:

df = df.loc[df['a b'] == 5]

Since you are only filtering rows, you can omit .loc accessor altogether:

df = df[df['a b'] == 5]

Answer 3

It is not possible yet. Check github issue #6508:

Note that in reality .query is just a nice-to-have interface, in fact it has very specific guarantees, meaning its meant to parse like a query language, and not a fully general interface.

Reason is for query need string to be a valid python expression, so column names must be valid python identifiers.

Solution is boolean indexing:

df = df[df['a b'] == 5]

Answer 4

I am afraid that the query method does not accept column name with empty space. In any case you can query the dataframe in this way:

import pandas as pd
a = pd.DataFrame({'a b':[1,2,3,4], 'c':[5,6,7,8]})
a[a['a b']==1]

Answer 5

Easier way:

df[
    (df['column name 1'] == 10)
    & (df['column name 2'] > 4)
]

Answer 6

Instead of using the pandas.query function I would create a condition in this case to lookup values and where the condition is True. For example:

import pandas as pd
a = pd.DataFrame(columns=["a b", "c"])
a["a b"] = [1,2,3,5]
a["c"] = [5,6,7,8]
#a.query('a b==5') Remove the query because it cannot lookup columns with spaces in the name.

condition = a['a b'] == 5
print(a['a b'][condition])

output:

    3    5

We see that at index 3 your condition evaluates to True (if you want the specific index and not Series of Boolean values).