Quantipy is an open-source data processing, analysis and reporting software project that builds on the excellent pandas and numpy libraries. Aimed at people data, Quantipy offers support for native handling of special data types like multiple choice variables, statistical analysis using case or observation weights, DataFrame metadata and pretty data exports.
Batch defintionsEfforts are underway to port Quantipy to Python 3 in a seperate repository.
View the documentation at readthedocs.org
We recommend installing Anaconda for Python 2.7 which will provide most of the required libraries and an easy means of keeping them up-to-date over time.
Dependencies numpy and scipy are handled by conda. Create a virtual environment:
conda create -n envqp python=2.7 numpy==1.11.3 scipy==0.18.1Install in editable mode:
pip install -r requirements_dev.txtDependencies numpy and scipy are handled in the installation.
Create a virtual environment:
conda create -n envqp python=2.7Install in editable mode:
pip install -r requirements_dev.txtGet started
Start a new folder called 'Quantipy-5' and add a subfolder called 'data'.
You can find an example dataset in quantipy/tests:
Put these files into your 'data' folder.
Start with some import statements:
import pandas as pd
import quantipy as qp
from quantipy.core.tools.dp.prep import frange
# This is a handy bit of pandas code to let you display your dataframes
# without having them split to fit a vertical column.
pd.set_option('display.expand_frame_repr', False)Load, inspect and edit your data
Load the input files in a qp.DataSet instance and inspect the metadata
with methods like .variables(), .meta() or .crosstab():
# Define the paths of your input files
path_json = './data/Example Data (A).json'
path_csv = './data/Example Data (A).csv'
dataset = qp.DataSet('Example Data (A)')
dataset.read_quantipy(path_json, path_csv)
dataset.crosstab('q2', text=True)Question q2. Which, if any, of these other sports have you ever participated in?
Values @
Question Values
q2. Which, if any, of these other sports have y... All 2999.0
Sky diving 1127.0
Base jumping 1366.0
Mountain biking 1721.0
Kite boarding 649.0
Snowboarding 458.0
Parachuting 428.0
Other 492.0
None of these 53.0Variables can be created, recoded or edited with DataSet methods, e.g. derive():
mapper = [(1, 'Any sports', {'q2': frange('1-6, 97')}),
(98, 'None of these', {'q2': 98})]
dataset.derive('q2_rc', 'single', dataset.text('q2'), mapper)
dataset.meta('q2_rc')single codes texts missing
q2_rc: Which, if any, of these other sports hav...
1 1 Any sports None
2 98 None of these NoneThe DataSet case data component can be inspected with the []-indexer, as known from a pd.DataFrame:
dataset[['q2', 'q2_rc']].head(5) q2 q2_rc
0 1;2;3;5; 1.0
1 3;6; 1.0
2 NaN NaN
3 NaN NaN
4 NaN NaNAnalyse and create aggregations batchwise
A qp.Batch as a subclass of qp.DataSet is a container for structuring
data analysis and aggregation specifications:
batch = dataset.add_batch('batch1')
batch.add_x(['q2', 'q2b', 'q5'])
batch.add_y(['gender', 'q2_rc'])The batch definitions are stored in dataset._meta['sets']['batches']['batch1'].
A qp.Stack can be created and populated based on the available qp.Batch
definitions stored in the qp.DataSet:
stack = dataset.populate()
stack.describe() data filter x y view #
0 Example Data (A) no_filter q2b @ NaN 1
1 Example Data (A) no_filter q2b q2_rc NaN 1
2 Example Data (A) no_filter q2b gender NaN 1
3 Example Data (A) no_filter q2 @ NaN 1
4 Example Data (A) no_filter q2 q2_rc NaN 1
5 Example Data (A) no_filter q2 gender NaN 1
6 Example Data (A) no_filter q5 @ NaN 1
7 Example Data (A) no_filter q5_3 @ NaN 1
8 Example Data (A) no_filter q5_3 q2_rc NaN 1
9 Example Data (A) no_filter q5_3 gender NaN 1
10 Example Data (A) no_filter q5_2 @ NaN 1
11 Example Data (A) no_filter q5_2 q2_rc NaN 1
12 Example Data (A) no_filter q5_2 gender NaN 1
13 Example Data (A) no_filter q5_1 @ NaN 1
14 Example Data (A) no_filter q5_1 q2_rc NaN 1
15 Example Data (A) no_filter q5_1 gender NaN 1
16 Example Data (A) no_filter q5_6 @ NaN 1
17 Example Data (A) no_filter q5_6 q2_rc NaN 1
18 Example Data (A) no_filter q5_6 gender NaN 1
19 Example Data (A) no_filter q5_5 @ NaN 1
20 Example Data (A) no_filter q5_5 q2_rc NaN 1
21 Example Data (A) no_filter q5_5 gender NaN 1
22 Example Data (A) no_filter q5_4 @ NaN 1
23 Example Data (A) no_filter q5_4 q2_rc NaN 1
24 Example Data (A) no_filter q5_4 gender NaN 1Each of the defintions is a qp.Link. These can be e.g. analyzed in various ways,
e.g. grouped categories can be calculated using the engine qp.Quantity:
link = stack[dataset.name]['no_filter']['q2']['q2_rc']
q = qp.Quantity(link)
q.group(frange('1-6, 97'), axis='x', expand='after')
q.count()Question q2_rc
Values All 1 98
Question Values
q2 All 2999.0 2946.0 53.0
net 2946.0 2946.0 0.0
1 1127.0 1127.0 0.0
2 1366.0 1366.0 0.0
3 1721.0 1721.0 0.0
4 649.0 649.0 0.0
5 458.0 458.0 0.0
6 428.0 428.0 0.0
97 492.0 492.0 0.0We can also simply add so called qp.Views to the whole of the qp.Stack:
stack.aggregate(['counts', 'c%'], False, verbose=False)
stack.add_stats('q2b', stats=['mean'], rescale={1: 100, 2:50, 3:0}, verbose=False)
stack.describe('view', 'x')x q2 q2b q5 q5_1 q5_2 q5_3 q5_4 q5_5 q5_6
view
x|d.mean|x[{100,50,0}]:|||stat NaN 3.0 NaN NaN NaN NaN NaN NaN NaN
x|f|:|y||c% 3.0 3.0 1.0 3.0 3.0 3.0 3.0 3.0 3.0
x|f|:|||counts 3.0 3.0 1.0 3.0 3.0 3.0 3.0 3.0 3.0link = stack[dataset.name]['no_filter']['q2b']['q2_rc']
link['x|d.mean|x[{100,50,0}]:|||stat']Question q2_rc
Values 1 98
Question Values
q2b mean 52.354167 43.421053There is so much more you can do with Quantipy... why don't you explore the docs to find out!