How to pass through a list of queries to a pandas dataframe, and output the list of results?

Question

When selecting rows whose column value column_name equals a scalar, some_value, we use ==:

df.loc[df['column_name'] == some_value]

or use .query()

df.query('column_name == some_value')

In a concrete example:

import pandas as pd
import numpy as np
df = pd.DataFrame({'Col1': 'what are men to rocks and mountains'.split(),
                   'Col2': 'the curves of your lips rewrite history.'.split(),
                   'Col3': np.arange(7),
                   'Col4': np.arange(7) * 8})

print(df)

         Col1      Col2  Col3  Col4
0       what       the     0     0
1        are    curves     1     8
2        men        of     2    16
3         to      your     3    24
4      rocks      lips     4    32
5        and   rewrite     5    40
6  mountains  history      6    48

A query could be

rocks_row = df.loc[df['Col1'] == "rocks"]

which outputs

print(rocks_row)
    Col1  Col2  Col3  Col4
4  rocks  lips     4    32

I would like to pass through a list of values to query against a dataframe, which outputs a list of "correct queries".

The queries to execute would be in a list, e.g.

list_match = ['men', 'curves', 'history']

which would output all rows which meet this condition, i.e.

matches = pd.concat([df1, df2, df3]) 

where

df1 = df.loc[df['Col1'] == "men"]

df2 = df.loc[df['Col1'] == "curves"]

df3 = df.loc[df['Col1'] == "history"]

My idea would be to create a function that takes in a

output = []
def find_queries(dataframe, column, value, output):
    for scalar in value: 
        query = dataframe.loc[dataframe[column] == scalar]]
        output.append(query)    # append all query results to a list
    return pd.concat(output)    # return concatenated list of dataframes

However, this appears to be exceptionally slow, and doesn't actually take advantage of the pandas data structure. What is the "standard" way to pass through a list of queries through a pandas dataframe?

EDIT: How does this translate into "more complex" queries in pandas? e.g. where with an HDF5 document?

df.to_hdf('test.h5','df',mode='w',format='table',data_columns=['A','B'])

pd.read_hdf('test.h5','df')

pd.read_hdf('test.h5','df',where='A=["foo","bar"] & B=1')

Answer 1

If I understood your question correctly you can do it either using boolean indexing as @uhjish has already shown in his answer or using query() method:

In [30]: search_list = ['rocks','mountains']

In [31]: df
Out[31]:
        Col1      Col2  Col3  Col4
0       what       the     0     0
1        are    curves     1     8
2        men        of     2    16
3         to      your     3    24
4      rocks      lips     4    32
5        and   rewrite     5    40
6  mountains  history.     6    48

.query() method:

In [32]: df.query('Col1 in @search_list and Col4 > 40')
Out[32]:
        Col1      Col2  Col3  Col4
6  mountains  history.     6    48

In [33]: df.query('Col1 in @search_list')
Out[33]:
        Col1      Col2  Col3  Col4
4      rocks      lips     4    32
6  mountains  history.     6    48

using boolean indexing:

In [34]: df.ix[df.Col1.isin(search_list) & (df.Col4 > 40)]
Out[34]:
        Col1      Col2  Col3  Col4
6  mountains  history.     6    48

In [35]: df.ix[df.Col1.isin(search_list)]
Out[35]:
        Col1      Col2  Col3  Col4
4      rocks      lips     4    32
6  mountains  history.     6    48

UPDATE: using function:

def find_queries(df, qry, debug=0, **parms):
    if debug:
        print('[DEBUG]: Query:\t' + qry.format(**parms))
    return df.query(qry.format(**parms))

In [31]: find_queries(df, 'Col1 in {Col1} and Col4 > {Col4}', Col1='@search_list', Col4=40)
    ...:
Out[31]:
        Col1      Col2  Col3  Col4
6  mountains  history.     6    48

In [32]: find_queries(df, 'Col1 in {Col1} and Col4 > {Col4}', Col1='@search_list', Col4=10)
Out[32]:
        Col1      Col2  Col3  Col4
4      rocks      lips     4    32
6  mountains  history.     6    48

including debugging info (print query):

In [40]: find_queries(df, 'Col1 in {Col1} and Col4 > {Col4}', Col1='@search_list', Col4=10, debug=1)
[DEBUG]: Query: Col1 in @search_list and Col4 > 10
Out[40]:
        Col1      Col2  Col3  Col4
4      rocks      lips     4    32
6  mountains  history.     6    48

Answer 2

The best way to deal with this is by indexing into the rows using a Boolean series as you would in R.

Using your df as an example,

In [5]: df.Col1 == "what"
Out[5]:
0     True
1    False
2    False
3    False
4    False
5    False
6    False
Name: Col1, dtype: bool

In [6]: df[df.Col1 == "what"]
Out[6]:
   Col1 Col2  Col3  Col4
0  what  the     0     0

Now we combine this with the pandas isin function.

In [8]: df[df.Col1.isin(["men","rocks","mountains"])]
Out[8]:
        Col1      Col2  Col3  Col4
2        men        of     2    16
4      rocks      lips     4    32
6  mountains  history.     6    48

To filter on multiple columns we can chain them together with & and | operators like so.

In [10]: df[df.Col1.isin(["men","rocks","mountains"]) | df.Col2.isin(["lips","your"])]
Out[10]:
        Col1      Col2  Col3  Col4
2        men        of     2    16
3         to      your     3    24
4      rocks      lips     4    32
6  mountains  history.     6    48

In [11]: df[df.Col1.isin(["men","rocks","mountains"]) & df.Col2.isin(["lips","your"])]
Out[11]:
    Col1  Col2  Col3  Col4
4  rocks  lips     4    32