Currently i am having an question in python pandas. I want to filter a dataframe using url query string dynamically.
For eg: CSV:
url: http://example.com/filter?Name=Sam&Age=21&Gender=male
Hardcoded:
filtered_data = data[
(data['Name'] == 'Sam') &
(data['Age'] == 21) &
(data['Gender'] == 'male')
];
I don't want to hard code the filter keys like before because the csv file changes anytime with different column headers. Any suggestions
The easiest way to create this filter dynamically is probably to use np.all.
For example:
import numpy as np
query = {'Name': 'Sam', 'Age': 21, 'Gender': 'male'}
filters = [data[k] == v for k, v in query.items()]
filter_data = data[np.all(filters, axis=0)]
use df.query. For example
df = pd.read_csv(url)
conditions = "Name == 'Sam' and Age == 21 and Gender == 'Male'"
filtered_data = df.query(conditions)
You can build the conditions string dynamically using string formatting like
conditions = " and ".join("{} == {}".format(col, val)
for col, val in zip(df.columns, values)
Typically, your web framework will return the arguments in a dict-like structure. Let's say your args are like this:
args = {
'Name': ['Sam'],
'Age': ['21'], # Note that Age is a string
'Gender': ['male']
}
You can filter your dataset successively like this:
for key, values in args.items():
data = data[data[key].isin(values)]
However, this is likely not to match any data for Age, which may have been loaded as an integer. In that case, you could load the CSV file as a string via pd.read_csv(filename, dtype=object), or convert to string before comparison:
for key, values in args.items():
data = data[data[key].astype(str).isin(values)]
Incidentally, this will also match multiple values. For example, take the URL http://example.com/filter?Name=Sam&Name=Ben&Age=21&Gender=male -- which leads to the structure:
args = {
'Name': ['Sam', 'Ben'], # There are 2 names
'Age': ['21'],
'Gender': ['male']
}
In this case, both Ben and Sam will be matched, since we're using .isin to match.
type of each columns. eg Age
