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    "info": {
        "author": "Manuel Martinez",
        "author_email": "manmartgarc@gmail.com",
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            "License :: OSI Approved :: MIT License",
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        "description": "# Stochatreat\n## Introduction\nThis is a Python module to employ block randomization using pandas. Mainly thought with RCTs in mind, it also works for any other scenario in where you would like to randomly allocate treatment within *blocks* or *strata*.\n\n## Installation\n```\npip install stochatreat\n```\n\n## Usage\nSingle cluster:\n```python\nfrom stochatreat import stochatreat\nimport numpy as np\nimport pandas as pd\n\n# make 1000 households in 5 different neighborhoods.\nnp.random.seed(42)\ndf = pd.DataFrame(data={'id': list(range(1000)),\n                        'nhood': np.random.randint(1, 6, size=1000)})\n\n# randomly assign treatments by neighborhoods.\ntreats = stochatreat(data=df,              # your dataframe\n                     block_cols='nhood',   # the blocking variable\n                     treats=2,             # including control\n                     idx_col='id',         # the unique id column\n                     random_state=42)\n# merge back with original data\ndf = df.merge(treats, how='left', on='id')\n\n# check for allocations\ndf.groupby('nhood')['treat'].value_counts().unstack()\n\n# previous code should return this\ntreat  0.0  1.0\nnhood          \n1      105  105\n2       95   95\n3       95   95\n4      103  103\n5      102  102\n```\n\nMultiple clusters and treatment probabilities:\n```python\nfrom stochatreat import stochatreat\nimport numpy as np\nimport pandas as pd\n\n# make 1000 households in 5 different neighborhoods, with a dummy indicator\nnp.random.seed(42)\ndf = pd.DataFrame(data={'id': list(range(1000)),\n                        'nhood': np.random.randint(1, 6, size=1000),\n                        'dummy': np.random.randint(0, 2, size=1000)})\n\n# randomly assign treatments by neighborhoods and dummy status.\ntreats = stochatreat(data=df,\n                     block_cols=['nhood', 'dummy'],\n                     treats=2,\n                     probs=[1/3, 2/3],\n                     idx_col='id',\n                     random_state=42)\n# merge back with original data\ndf = df.merge(treats, how='left', on='id')\n\n# check for allocations\ndf.groupby(['nhood', 'dummy'])['treat'].value_counts().unstack()\n\n# previous code should return this\ntreat        0.0  1.0\nnhood dummy          \n1     0       38   74\n      1       33   65\n2     0       35   69\n      1       29   57\n3     0       30   58\n      1       34   68\n4     0       36   72\n      1       33   65\n5     0       34   67\n      1       35   68\n```\n\n## Acknowledgments\n- `stochatreat` is totally inspired by [Alvaro Carril's](https://acarril.github.io/) fantastic Stata package: [`randtreat`](https://acarril.github.io/posts/randtreat), which was published in [The Stata Journal](https://www.stata-journal.com/article.html?article=st0490) :trumpet:.\n- [David McKenzie's](http://blogs.worldbank.org/impactevaluations/tools-of-the-trade-doing-stratified-randomization-with-uneven-numbers-in-some-strata) fantastic post (and blog) about running RCTs for the World Bank.\n- [*In Pursuit of Balance: Randomization in Practice in Development Field Experiments.* Bruhn, McKenzie, 2009](https://www.aeaweb.org/articles?id=10.1257/app.1.4.200)\n\n\n",
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