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

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

pandas.DataFrame.query to allow column name with space · Issue , From pandas 0.25 onward you will be able to escape column names with backticks so you can do a.query('`a b` == 5'). Query function gives error if column names have front slash #12858 Users shouldn't have to do this and it's a serious problem of pandas. DG.

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]

pandas query not equal, Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Learn more Pandas DataFrame: access multiple items with not equal to, =! Now, how can I select all trains except DeutscheBahn and British Rails and SNCF. How can I simultaneously choose the items not these?

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]

pandas query vs loc, The query function seams more efficient than the loc function. DF2: 2K records x 6 columns. The loc function seams much more efficient than the query function. Both queries return a single record. The simulation was done by running the same operation in a loop 10K times. Running python 2.7 and pandas 0.16.0. Any recommendations to improve the query speed? Motivating query() and eval() : Compound Expressions¶. We've seen previously that NumPy and Pandas support fast vectorized operations; for example, when 

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]

pandas query date, You can use pd.Timestamp to perform a query and a local reference. import pandas as pd import numpy as np df = pd.DataFrame() ts = pd.Timestamp df['date'] = np.array(np.arange(10) + datetime.now().timestamp(), dtype='M8[s]') print(df) print(df.query('date > @ts("20190515T071320")') with the output A step-by-step Python code example that shows how to select Pandas DataFrame rows between two First, lets ensure the 'birth_date' column is in date format.

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).

pandas read_csv column names with spaces, Thank for the comment. I normally use dot to access my columns (df.col_name) but just know this trick to access the column names with space by using df[column name with space"]. Thx. – theteddyboy Oct 12 '16 at 10:31 Why do they have to make the column names uppercase, with spaces, and whitespace all around? Do they like doing this to you? They probably