gss-data-analysis-gender-pay-gap.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# # This Python 3 environment comes with many helpful analytics libraries installed\n# # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# # For example, here's several helpful packages to load\n\n# import numpy as np # linear algebra\n# import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# # Input data files are available in the read-only \"../input/\" directory\n# # For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n# import os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# # You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# # You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install pingouin\n# import pingouin as pg","execution_count":3,"outputs":[{"output_type":"stream","text":"Collecting pingouin\n  Downloading pingouin-0.3.6.tar.gz (219 kB)\n\u001b[K     |████████████████████████████████| 219 kB 2.8 MB/s eta 0:00:01\n\u001b[?25hRequirement already satisfied: numpy>=1.15 in /opt/conda/lib/python3.7/site-packages (from pingouin) (1.18.5)\nRequirement already satisfied: scipy>=1.3 in /opt/conda/lib/python3.7/site-packages (from pingouin) (1.4.1)\nRequirement already satisfied: pandas>=0.24 in /opt/conda/lib/python3.7/site-packages (from pingouin) (1.0.3)\nRequirement already satisfied: matplotlib>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from pingouin) (3.2.1)\nRequirement already satisfied: seaborn>=0.9.0 in /opt/conda/lib/python3.7/site-packages (from pingouin) (0.10.0)\nRequirement already satisfied: statsmodels>=0.10.0 in /opt/conda/lib/python3.7/site-packages (from pingouin) (0.11.1)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from pingouin) (0.23.1)\nCollecting pandas_flavor>=0.1.2\n  Downloading pandas_flavor-0.2.0-py2.py3-none-any.whl (6.6 kB)\nCollecting outdated\n  Downloading outdated-0.2.0.tar.gz (4.0 kB)\nRequirement already satisfied: tabulate in /opt/conda/lib/python3.7/site-packages (from pingouin) (0.8.7)\nRequirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.24->pingouin) (2019.3)\nRequirement already satisfied: python-dateutil>=2.6.1 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.24->pingouin) (2.8.1)\nRequirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (2.4.7)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (0.10.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib>=3.0.2->pingouin) (1.2.0)\nRequirement already satisfied: patsy>=0.5 in /opt/conda/lib/python3.7/site-packages (from statsmodels>=0.10.0->pingouin) (0.5.1)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->pingouin) (2.1.0)\nRequirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->pingouin) (0.14.1)\nRequirement already satisfied: xarray in /opt/conda/lib/python3.7/site-packages (from pandas_flavor>=0.1.2->pingouin) (0.15.1)\nCollecting littleutils\n  Downloading littleutils-0.2.2.tar.gz (6.6 kB)\nRequirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from outdated->pingouin) (2.23.0)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas>=0.24->pingouin) (1.14.0)\nRequirement already satisfied: setuptools>=41.2 in /opt/conda/lib/python3.7/site-packages (from xarray->pandas_flavor>=0.1.2->pingouin) (46.1.3.post20200325)\nRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->outdated->pingouin) (1.24.3)\nRequirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.7/site-packages (from requests->outdated->pingouin) (3.0.4)\nRequirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->outdated->pingouin) (2.9)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->outdated->pingouin) (2020.6.20)\nBuilding wheels for collected packages: pingouin, outdated, littleutils\n  Building wheel for pingouin (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for pingouin: filename=pingouin-0.3.6-py3-none-any.whl size=217879 sha256=850dddcf664faad4986f69d680c3121962eb33bd88507430761a4673d3b3a882\n  Stored in directory: /root/.cache/pip/wheels/2c/1b/b4/4bb168adc02a7d28bc0b9265a5d8532ccbf140694602e76af2\n  Building wheel for outdated (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for outdated: filename=outdated-0.2.0-py3-none-any.whl size=4960 sha256=f91bd7b94531375db6f873c489e5b26dfdc62020a0681ac4706c87ff54578295\n  Stored in directory: /root/.cache/pip/wheels/6f/cd/a2/e49170b2cf59e88b952f3414f25a54d9f16f033bded4aaab26\n  Building wheel for littleutils (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for littleutils: filename=littleutils-0.2.2-py3-none-any.whl size=7048 sha256=bcf74a1991dc3e2f0abf5f645f68d17d5ae53b10e920020b475a555515ca843b\n  Stored in directory: /root/.cache/pip/wheels/d6/64/cd/32819b511a488e4993f2fab909a95330289c3f4e0f6ef4676d\nSuccessfully built pingouin outdated littleutils\nInstalling collected packages: pandas-flavor, littleutils, outdated, pingouin\nSuccessfully installed littleutils-0.2.2 outdated-0.2.0 pandas-flavor-0.2.0 pingouin-0.3.6\n","name":"stdout"}]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport scipy.stats as ss\nimport math\nimport pingouin as pg\n\n%matplotlib inline\n%config InlineBackend.figure_format='retina'\n\ndata = pd.read_csv(\"/kaggle/input/general-social-survey-gss-data/GSS_transformed_v1.csv\")\ndata = data[data.year == 2018]\n\ndef transform_age(x):\n    if x['age'] in [\"Don't know\",\"No answer\"]:\n        return -1\n    elif x['age'] in ['89 or older']:\n        return 89\n    else: \n        return int(x['age'])\n\ndef transform_uscitzn(x):\n    if x['uscitzn'] in ['A u.s. citizen','Born outside of the united states to parents who were u.s citizens at that time (if volunteered)',\n                        'A u.s. citizen born in puerto rico, the u.s. virgin islands, or the northern marianas islands']:\n        return 1\n    elif x['uscitzn'] in ['Not a u.s. citizen']:\n        return 0\n    else: \n        return -1\n\ndata['age'] = data.apply(transform_age, axis=1)\ndata['uscitzn_ind'] = data.apply(transform_uscitzn, axis=1)\n\ndata = data[data.conrinc > 0][['id_','age','sex','race','conrinc','uscitzn','uscitzn_ind']]","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.shape","execution_count":5,"outputs":[{"output_type":"execute_result","execution_count":5,"data":{"text/plain":"(1363, 7)"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.sex.value_counts()","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"Female    717\nMale      646\nName: sex, dtype: int64"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"group_a = \"Group A (All Men)\"\ngroup_b = \"Group B (All Women)\"\n\ndata_a = data[(data.sex.isin(['Male'])) & (data.conrinc > 0)]\nprint(\"{} Population -> size:{}, mean: {}, std: {} \"\n.format(group_a, len(data_a), round(data_a.conrinc.mean(),2), round(data_a.conrinc.std(),2)))\n\ndata_b = data[(data.sex.isin(['Female'])) & (data.conrinc > 0)]\nprint(\"{} Population -> size:{}, mean: {}, std: {} \"\n.format(group_b, len(data_b), round(data_b.conrinc.mean(),2), round(data_b.conrinc.std(),2)))","execution_count":7,"outputs":[{"output_type":"stream","text":"Group A (All Men) Population -> size:646, mean: 44527.22, std: 41199.96 \nGroup B (All Women) Population -> size:717, mean: 29862.12, std: 31593.19 \n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## EXPERIMENT-1: ALL MEN AND WOMEN\n\n# group_a = \"Group A (All Men)\"\n# group_b = \"Group B (All Women)\"\n\n# data_a = data[(data.sex.isin(['Male'])) & (data.conrinc > 0)]\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_a, len(data_a), round(data_a.conrinc.mean(),2), round(data_a.conrinc.std(),2)))\n\n# data_b = data[(data.sex.isin(['Female'])) & (data.conrinc > 0)]\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_b, len(data_b), round(data_b.conrinc.mean(),2), round(data_b.conrinc.std(),2)))\n\n\n## EXPERIMENT-2: ALL WHITE MEN AND WOMEN\n\n# group_a = \"Group A (All White Men)\"\n# group_b = \"Group B (All White Women)\"\n\n# data_a = data[(data.sex.isin(['Male'])) & (data.conrinc > 0) & (data.race.isin(['White']))]\n# data_b = data[(data.sex.isin(['Female'])) & (data.conrinc > 0) & (data.race.isin(['White']))]\n\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_a, len(data_a), round(data_a.conrinc.mean(),2), round(data_a.conrinc.std(),2)))\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_b, len(data_b), round(data_b.conrinc.mean(),2), round(data_b.conrinc.std(),2)))\n\n## EXPERIMENT-3: ALL BLACK MEN AND WOMEN\n\n# group_a = \"Group A (All Black Men)\"\n# group_b = \"Group B (All Black Women)\"\n\n# data_a = data[(data.sex.isin(['Male'])) & (data.conrinc > 0) & (data.race.isin(['Black']))]\n# data_b = data[(data.sex.isin(['Female'])) & (data.conrinc > 0) & (data.race.isin(['Black']))]\n\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_a, len(data_a), round(data_a.conrinc.mean(),2), round(data_a.conrinc.std(),2)))\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_b, len(data_b), round(data_b.conrinc.mean(),2), round(data_b.conrinc.std(),2)))\n\n\n## EXPERIMENT-4: ALL IMMIGRANT MEN AND WOMEN\n\n# group_a = \"Group A (All Immigrant Men)\"\n# group_b = \"Group B (All Immigrant Women)\"\n\n# data_a = data[(data.sex.isin(['Male'])) & (data.conrinc > 0) & (data.uscitzn_ind == 0)]\n# data_b = data[(data.sex.isin(['Female'])) & (data.conrinc > 0) & (data.uscitzn_ind == 0)]\n\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_a, len(data_a), round(data_a.conrinc.mean(),2), round(data_a.conrinc.std(),2)))\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_b, len(data_b), round(data_b.conrinc.mean(),2), round(data_b.conrinc.std(),2)))\n\n\n## EXPERIMENT-5: ALL YOUNG ADULT (MILLENIEAL'S AGE 20-35) MEN AND WOMEN\n\n# group_a = \"Group A (All Milenneal Men of age 20-38)\"\n# group_b = \"Group B (All Milenneal Women of age 20-38)\"\n\n# data_a = data[(data.sex.isin(['Male'])) & (data.conrinc > 0) & (data.age.between(20,38))]\n# data_b = data[(data.sex.isin(['Female'])) & (data.conrinc > 0) & (data.age.between(20,38))]\n\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_a, len(data_a), round(data_a.conrinc.mean(),2), round(data_a.conrinc.std(),2)))\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_b, len(data_b), round(data_b.conrinc.mean(),2), round(data_b.conrinc.std(),2)))\n\n\n## EXPERIMENT-6: ALL MIDDLE-AGED ADULT (AGE 39-59) MEN AND WOMEN\n\n# group_a = \"Group A (All Men of age 39-59)\"\n# group_b = \"Group B (All Women of age 39-59)\"\n\n# data_a = data[(data.sex.isin(['Male'])) & (data.conrinc > 0) & (data.age.between(39,59))]\n# data_b = data[(data.sex.isin(['Female'])) & (data.conrinc > 0) & (data.age.between(39,59))]\n\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_a, len(data_a), round(data_a.conrinc.mean(),2), round(data_a.conrinc.std(),2)))\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_b, len(data_b), round(data_b.conrinc.mean(),2), round(data_b.conrinc.std(),2)))\n\n## EXPERIMENT-7: ALL OLD-AGED ADULT (AGE 60+) MEN AND WOMEN\n\n# group_a = \"Group A (All Men of age 60+)\"\n# group_b = \"Group B (All Women of age 60+)\"\n\n# data_a = data[(data.sex.isin(['Male'])) & (data.conrinc > 0) & (data.age >= 60)]\n# data_b = data[(data.sex.isin(['Female'])) & (data.conrinc > 0) & (data.age >= 60)]\n\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_a, len(data_a), round(data_a.conrinc.mean(),2), round(data_a.conrinc.std(),2)))\n# print(\"{} Population -> size:{}, mean: {}, std: {} \"\n# .format(group_b, len(data_b), round(data_b.conrinc.mean(),2), round(data_b.conrinc.std(),2)))\n","execution_count":8,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Population Distribution"},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.rcParams['font.size'] = 6\nplt.figure(figsize=(10, 3))\nplt.suptitle(\"\"\" Population Income Distribution\"\"\")\n\nax1 = plt.subplot(1, 2, 1)\nax1 = sns.distplot(data_a.conrinc, hist=True, color=\"b\", bins=35)\nplt.axvline(x=data_a.conrinc.mean(), linewidth=1, ls = \"--\",color='r', label=\"mean\")\nplt.legend([\"mean\"])\nplt.xlabel(\"\"\" {}\nPop size:{}, Pop Mean: {}, Pop. Stddev: {}\"\"\".format(group_a, len(data_a), round(data_a.conrinc.mean(),2), round(data_a.conrinc.std(),2)))\n\nax2 = plt.subplot(1, 2, 2)\nax2 = sns.distplot(data_b.conrinc, hist=True, color=\"b\", bins=35)\nplt.axvline(x=data_b.conrinc.mean(), linewidth=1, ls = \"--\",color='r', label=\"mean\")\nplt.legend([\"mean\"])\nplt.xlabel(\"\"\" {}\nPop size: {}, Pop Mean: {}, Pop. Stddev: {}\"\"\".format(group_b, len(data_b), round(data_b.conrinc.mean(),2), round(data_b.conrinc.std(),2)))\n\nplt.show()","execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x216 with 2 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":588,"height":230},"needs_background":"light"}}]},{"metadata":{},"cell_type":"markdown","source":"### Sample size"},{"metadata":{"trusted":true},"cell_type":"code","source":"import math\nimport scipy.stats as ss\n\ndef calc_sample_size(population_size, confidence_level, margin_error):\n    alpha = 1 - (confidence_level)\n    Z = ss.norm.ppf(1 - (alpha/2))\n    p = 0.5\n    e = margin_error\n    N = population_size\n    \n    # Calc sample size \n    # Cochran’s Sample Size Formula\n    n_0 = ((Z**2) * p * (1-p)) / (e**2)\n    \n    # Adjustment for finite population\n    n = n_0 / (1 + ((n_0 - 1) / float(N)) )\n\n    return int(math.ceil(n))","execution_count":10,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_size_for_group_a = calc_sample_size(len(data_a), 0.95, 0.05)\nsample_size_for_group_b = calc_sample_size(len(data_b), 0.95, 0.05)\n\nprint(sample_size_for_group_a, sample_size_for_group_b)","execution_count":11,"outputs":[{"output_type":"stream","text":"242 251\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### Sampling Distributions"},{"metadata":{"trusted":true},"cell_type":"code","source":"def create_sample_distributions(pop_data, sample_size):\n    sample = []\n    for i in range(1000):\n        sample_mean = (pop_data.sample(n=sample_size, replace=True).conrinc.mean())\n        sample.append(sample_mean)\n    sample = np.asarray(sample)\n    return sample","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data_a_sample = create_sample_distributions(data_a, sample_size_for_group_a)\ndata_a_sample = np.asarray(data_a_sample)\n\ndata_b_sample = create_sample_distributions(data_b, sample_size_for_group_b)\ndata_b_sample = np.asarray(data_b_sample)\n\nstandard_error_for_group_a = round((data_a.conrinc.std()/np.sqrt(sample_size_for_group_a)),2)\nstandard_error_for_group_b = round((data_b.conrinc.std()/np.sqrt(sample_size_for_group_b)),2)","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# ## HISTOGRAM FOR SAMPLING DISTRIBUTION\n\n# plt.rcParams['font.size'] = 6\n# plt.figure(figsize=(10, 3))\n# plt.suptitle(\"\"\" Sample Means Income Distribution\"\"\")\n\n# ax1 = plt.subplot(1, 2, 1)\n# ax1 = sns.distplot(data_a_sample, hist=True, color=\"g\", bins=35)\n# plt.axvline(x=data_a_sample.mean(), linewidth=1, ls = \"--\",color='r', label=\"mean\")\n# plt.legend([\"mean\"])\n# plt.xlabel(\"\"\"{} \n# Sample size: {}, Std error: {}, Sample Mean: {}, Sample Stddev: {}\"\"\".format(group_a, sample_size_for_group_a, standard_error_for_group_a,\n# round(data_a_sample.mean(),2), round(data_a_sample.std(),2)))\n\n# ax2 = plt.subplot(1, 2, 2)\n# ax2 = sns.distplot(data_b_sample, hist=True, color=\"g\", bins=35)\n# plt.axvline(x=data_b_sample.mean(), linewidth=1, ls = \"--\",color='r', label=\"mean\")\n# plt.legend([\"mean\"])\n# plt.xlabel(\"\"\"{} \n# Sample size: {}, Std error: {}, Sample Mean: {}, Sample Stddev: {}\"\"\".format(group_b, sample_size_for_group_b, standard_error_for_group_b,\n# round(data_b_sample.mean(),2), round(data_b_sample.std(),2)))\n\n# plt.show()","execution_count":14,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"standard_error_for_group_a = round((data_a.conrinc.std()/np.sqrt(sample_size_for_group_a)),2)\nstandard_error_for_group_b = round((data_b.conrinc.std()/np.sqrt(sample_size_for_group_b)),2)\n\nprint(\"\"\"{} Sampling distribution:\nStandard Error for n={}: {}, Mean Income: {}, Standard Deviation: {}\"\"\"\n.format(group_a, sample_size_for_group_a, standard_error_for_group_a, round(data_a_sample.mean(),2), round(data_a_sample.std(),2)))\n\nprint(\"\"\"{} Sampling distribution:\nStandard Error for n={}: {}, Mean Income: {}, Standard Deviation: {}\"\"\"\n.format(group_b, sample_size_for_group_b, standard_error_for_group_b, round(data_b_sample.mean(),2), round(data_b_sample.std(),2)))\n\nprint('Difference in means: ', round(data_a_sample.mean() - data_b_sample.mean(),2))\nprint(\"Group B mean is {}% of Group A mean\".format(round(data_b_sample.mean()/data_a_sample.mean() * 100, 1)))","execution_count":15,"outputs":[{"output_type":"stream","text":"Group A (All Men) Sampling distribution:\nStandard Error for n=242: 2648.43, Mean Income: 44558.87, Standard Deviation: 2667.94\nGroup B (All Women) Sampling distribution:\nStandard Error for n=251: 1994.14, Mean Income: 29852.1, Standard Deviation: 1973.23\nDifference in means:  14706.77\nGroup B mean is 67.0% of Group A mean\n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### t-Test"},{"metadata":{"trusted":true},"cell_type":"code","source":"from scipy.stats import t, ttest_ind\n\nalpha = 0.05\nttest_result = ttest_ind(data_a_sample, data_b_sample)\ndegree_freedom = len(data_a_sample) + len(data_b_sample) - 2\ncritical_value = t.ppf(1 - alpha, degree_freedom)\n\nprint(\"\"\"t-Test with aplha={} for independent samples, {} and {}:\"\"\".format(alpha, group_a, group_b))\nprint('t=%.3f, df=%d, cv=%.3f, p=%.3f' % (ttest_result.statistic, degree_freedom, critical_value, ttest_result.pvalue))","execution_count":16,"outputs":[{"output_type":"stream","text":"t-Test with aplha=0.05 for independent samples, Group A (All Men) and Group B (All Women):\nt=140.080, df=1998, cv=1.646, p=0.000\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"## HISTOGRAM FOR SAMPLING DISTRIBUTION\n\nplt.rcParams['font.size'] = 6\nplt.figure(figsize=(10, 3))\nplt.suptitle(\"\"\" Sample Means Income Distribution \nt={} p={}\n\"\"\".format(round(ttest_result.statistic,2), round(ttest_result.pvalue,2)))\n\nax1 = plt.subplot(1, 2, 1)\nax1 = sns.distplot(data_a_sample, hist=True, color=\"g\", bins=35)\nplt.axvline(x=data_a_sample.mean(), linewidth=1, ls = \"--\",color='r', label=\"mean\")\nplt.legend([\"mean\"])\nplt.xlabel(\"\"\"{} \nSample size: {}, Std error: {}, Sample Mean: {}, Sample Stddev: {}\"\"\".format(group_a, sample_size_for_group_a, standard_error_for_group_a,\nround(data_a_sample.mean(),2), round(data_a_sample.std(),2)))\n\nax2 = plt.subplot(1, 2, 2)\nax2 = sns.distplot(data_b_sample, hist=True, color=\"g\", bins=35)\nplt.axvline(x=data_b_sample.mean(), linewidth=1, ls = \"--\",color='r', label=\"mean\")\nplt.legend([\"mean\"])\nplt.xlabel(\"\"\"{} \nSample size: {}, Std error: {}, Sample Mean: {}, Sample Stddev: {}\"\"\".format(group_b, sample_size_for_group_b, standard_error_for_group_b,\nround(data_b_sample.mean(),2), round(data_b_sample.std(),2)))\n\nplt.show()","execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 720x216 with 2 Axes>","image/png":"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\n"},"metadata":{"image/png":{"width":608,"height":230},"needs_background":"light"}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pingouin as pg\nmodel = pg.ttest(data_a_sample, data_b_sample)  # output is a pandas dataframe\nmodel.head()","execution_count":18,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/pingouin/bayesian.py:150: RuntimeWarning: divide by zero encountered in double_scalars\n  bf10 = 1 / ((1 + t**2 / df)**(-(df + 1) / 2) / integr)\n","name":"stderr"},{"output_type":"execute_result","execution_count":18,"data":{"text/plain":"                T   dof       tail  p-val                 CI95%   cohen-d  \\\nT-test  140.07974  1998  two-sided    0.0  [14500.87, 14912.67]  6.264556   \n\n       BF10  power  \nT-test  inf    1.0  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>T</th>\n      <th>dof</th>\n      <th>tail</th>\n      <th>p-val</th>\n      <th>CI95%</th>\n      <th>cohen-d</th>\n      <th>BF10</th>\n      <th>power</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>T-test</th>\n      <td>140.07974</td>\n      <td>1998</td>\n      <td>two-sided</td>\n      <td>0.0</td>\n      <td>[14500.87, 14912.67]</td>\n      <td>6.264556</td>\n      <td>inf</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Calculate Confidence interval for Difference of means"},{"metadata":{"trusted":true},"cell_type":"code","source":"n1 = sample_size_for_group_a\nn2 = sample_size_for_group_b\n\nmean_male = data_a_sample.mean()\nmean_female = data_b_sample.mean()\n\nsd_male = data_a_sample.std()\nsd_female = data_b_sample.std() \n\nse_male = sd_male / np.sqrt(sample_size_for_group_a)\nse_female = sd_female / np.sqrt(sample_size_for_group_b)\n\nmean_d = mean_male - mean_female\n\nsem_d = (np.sqrt((n1-1)*se_male**2 + (n2-1)*se_female**2)/(n1+n2-2))*(np.sqrt(1/n1 + 1/n2))\n\nlcb = mean_d - 1.96*sem_d\nucb = mean_d + 1.96*sem_d\n\nprint(\"({},{})\".format(round(lcb,2), round(ucb,2)))","execution_count":21,"outputs":[{"output_type":"stream","text":"(14705.58,14707.96)\n","name":"stdout"}]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":4}