{ "cells": [ { "cell_type": "markdown", "id": "79cd8c04", "metadata": {}, "source": [ "# Descriptive statistics\n", "\n", "pandas objects are equipped with a number of common mathematical and statistical methods. Most of them fall into the category of reductions or summary statistics, methods that extract a single value (such as the sum or mean) from a series or set of values from the rows or columns of a DataFrame. Compared to similar methods found in NumPy arrays, they also handle missing data." ] }, { "cell_type": "code", "execution_count": 1, "id": "1f4bf43c", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:35.634657Z", "iopub.status.busy": "2026-05-21T12:53:35.634317Z", "iopub.status.idle": "2026-05-21T12:53:35.909370Z", "shell.execute_reply": "2026-05-21T12:53:35.909046Z", "shell.execute_reply.started": "2026-05-21T12:53:35.634640Z" } }, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " 0 1 2\n", "2022-02-03 -1.118009 -0.596567 1.675063\n", "2022-02-04 0.622485 0.460062 -1.011463\n", "2022-02-05 -0.951856 0.188381 2.315514\n", "2022-02-06 0.916177 0.178374 0.207760\n", "2022-02-07 0.713193 -0.246814 1.469239\n", "2022-02-08 -0.847348 -0.328571 -1.491281\n", "2022-02-09 NaN NaN NaN" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "\n", "rng = np.random.default_rng()\n", "df = pd.DataFrame(\n", " rng.normal(size=(7, 3)),\n", " index=pd.date_range(\"2022-02-02\", periods=7),\n", ")\n", "new_index = pd.date_range(\"2022-02-03\", periods=7)\n", "df2 = df.reindex(new_index)\n", "\n", "df2" ] }, { "cell_type": "markdown", "id": "b1cb2575", "metadata": {}, "source": [ "Calling the `pandas.DataFrame.sum` method returns a series containing column totals:" ] }, { "cell_type": "code", "execution_count": 2, "id": "070b5cd2", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:35.909965Z", "iopub.status.busy": "2026-05-21T12:53:35.909821Z", "iopub.status.idle": "2026-05-21T12:53:35.912931Z", "shell.execute_reply": "2026-05-21T12:53:35.912648Z", "shell.execute_reply.started": "2026-05-21T12:53:35.909956Z" } }, "outputs": [ { "data": { "text/plain": [ "0 -0.665358\n", "1 -0.345136\n", "2 3.164833\n", "dtype: float64" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.sum()" ] }, { "cell_type": "markdown", "id": "b7884bef", "metadata": {}, "source": [ "Passing `axis='columns'` or `axis=1` instead sums over the columns:" ] }, { "cell_type": "code", "execution_count": 3, "id": "1293360b", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:35.913378Z", "iopub.status.busy": "2026-05-21T12:53:35.913298Z", "iopub.status.idle": "2026-05-21T12:53:35.915872Z", "shell.execute_reply": "2026-05-21T12:53:35.915656Z", "shell.execute_reply.started": "2026-05-21T12:53:35.913371Z" } }, "outputs": [ { "data": { "text/plain": [ "2022-02-03 -0.039513\n", "2022-02-04 0.071084\n", "2022-02-05 1.552039\n", "2022-02-06 1.302311\n", "2022-02-07 1.935618\n", "2022-02-08 -2.667200\n", "2022-02-09 0.000000\n", "Freq: D, dtype: float64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.sum(axis=\"columns\")" ] }, { "cell_type": "markdown", "id": "ec346926", "metadata": {}, "source": [ "If an entire row or column contains all NA values, the sum is `0`. This can be disabled with the `skipna` option:" ] }, { "cell_type": "code", "execution_count": 4, "id": "f698747c", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:35.916191Z", "iopub.status.busy": "2026-05-21T12:53:35.916126Z", "iopub.status.idle": "2026-05-21T12:53:35.918801Z", "shell.execute_reply": "2026-05-21T12:53:35.918591Z", "shell.execute_reply.started": "2026-05-21T12:53:35.916184Z" } }, "outputs": [ { "data": { "text/plain": [ "2022-02-03 -0.039513\n", "2022-02-04 0.071084\n", "2022-02-05 1.552039\n", "2022-02-06 1.302311\n", "2022-02-07 1.935618\n", "2022-02-08 -2.667200\n", "2022-02-09 NaN\n", "Freq: D, dtype: float64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.sum(axis=\"columns\", skipna=False)" ] }, { "cell_type": "markdown", "id": "644a61ca", "metadata": {}, "source": [ "Some aggregations, such as `mean`, require at least one non-`NaN` value to obtain a valuable result:" ] }, { "cell_type": "code", "execution_count": 5, "id": "1c799ec9", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:35.919141Z", "iopub.status.busy": "2026-05-21T12:53:35.919042Z", "iopub.status.idle": "2026-05-21T12:53:35.921505Z", "shell.execute_reply": "2026-05-21T12:53:35.921237Z", "shell.execute_reply.started": "2026-05-21T12:53:35.919130Z" } }, "outputs": [ { "data": { "text/plain": [ "2022-02-03 -0.013171\n", "2022-02-04 0.023695\n", "2022-02-05 0.517346\n", "2022-02-06 0.434104\n", "2022-02-07 0.645206\n", "2022-02-08 -0.889067\n", "2022-02-09 NaN\n", "Freq: D, dtype: float64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.mean(axis=\"columns\")" ] }, { "cell_type": "markdown", "id": "ce078db0", "metadata": {}, "source": [ "## Options for reduction methods\n", "\n", "Method | Description\n", ":----- | :----------\n", "`axis` | the axis of values to reduce: `0` for the rows of the DataFrame and `1` for the columns\n", "`skipna` | exclude missing values; by default `True`.\n", "`level` | reduce grouped by level if the axis is hierarchically indexed (MultiIndex)\n", "\n", "Some methods, such as `idxmin` and `idxmax`, provide indirect statistics such as the index value at which the minimum or maximum value is reached:" ] }, { "cell_type": "code", "execution_count": 6, "id": "aa6e4b54", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:35.922977Z", "iopub.status.busy": "2026-05-21T12:53:35.922890Z", "iopub.status.idle": "2026-05-21T12:53:35.925678Z", "shell.execute_reply": "2026-05-21T12:53:35.925482Z", "shell.execute_reply.started": "2026-05-21T12:53:35.922969Z" } }, "outputs": [ { "data": { "text/plain": [ "0 2022-02-06\n", "1 2022-02-04\n", "2 2022-02-05\n", "dtype: datetime64[ns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.idxmax()" ] }, { "cell_type": "markdown", "id": "ad8181d8", "metadata": {}, "source": [ "Other methods are accumulations:" ] }, { "cell_type": "code", "execution_count": 7, "id": "373b127a", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:35.926025Z", "iopub.status.busy": "2026-05-21T12:53:35.925968Z", "iopub.status.idle": "2026-05-21T12:53:35.929703Z", "shell.execute_reply": "2026-05-21T12:53:35.929397Z", "shell.execute_reply.started": "2026-05-21T12:53:35.926018Z" } }, "outputs": [ { "data": { "text/html": [ "
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count6.0000006.0000006.000000
mean-0.110893-0.0575230.527472
std0.9524390.3959491.545761
min-1.118009-0.596567-1.491281
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" ], "text/plain": [ " 0 1 2\n", "count 6.000000 6.000000 6.000000\n", "mean -0.110893 -0.057523 0.527472\n", "std 0.952439 0.395949 1.545761\n", "min -1.118009 -0.596567 -1.491281\n", "25% -0.925729 -0.308132 -0.706657\n", "50% -0.112431 -0.034220 0.838500\n", "75% 0.690516 0.185880 1.623607\n", "max 0.916177 0.460062 2.315514" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2.describe()" ] }, { "cell_type": "markdown", "id": "5d1f2a90", "metadata": {}, "source": [ "For non-numeric data, `describe` generates alternative summary statistics:" ] }, { "cell_type": "code", "execution_count": 9, "id": "4277fda3", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:35.936622Z", "iopub.status.busy": "2026-05-21T12:53:35.936532Z", "iopub.status.idle": "2026-05-21T12:53:35.940601Z", "shell.execute_reply": "2026-05-21T12:53:35.940325Z", "shell.execute_reply.started": "2026-05-21T12:53:35.936615Z" } }, "outputs": [ { "data": { "text/html": [ "
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CodeOctal
count66
unique65
topU+0000004
freq12
\n", "
" ], "text/plain": [ " Code Octal\n", "count 6 6\n", "unique 6 5\n", "top U+0000 004\n", "freq 1 2" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = {\n", " \"Code\": [\"U+0000\", \"U+0001\", \"U+0002\", \"U+0003\", \"U+0004\", \"U+0005\"],\n", " \"Octal\": [\"001\", \"002\", \"003\", \"004\", \"004\", \"005\"],\n", "}\n", "df3 = pd.DataFrame(data)\n", "\n", "df3.describe()" ] }, { "cell_type": "markdown", "id": "ce69eb28", "metadata": {}, "source": [ "Descriptive and summary statistics:\n", "\n", "Method | Description\n", ":----- | :----------\n", "`count` | number of non-NA values\n", "`describe` | calculation of a set of summary statistics for series or each DataFrame column\n", "`min`, `max` | calculation of minimum and maximum values\n", "`argmin`, `argmax` | calculation of the index points (integers) at which the minimum or maximum value was reached\n", "`idxmin`, `idxmax` | calculation of the index labels at which the minimum or maximum values were reached\n", "`quantile` | calculation of the sample quantile in the range from 0 to 1\n", "`sum` | sum of the values\n", "`mean` | arithmetic mean of the values\n", "`median` | arithmetic median (50% quantile) of the values\n", "`mad` | mean absolute deviation from the mean value\n", "`prod` | product of all values\n", "`var` | sample variance of the values\n", "`std` | sample standard deviation of the values\n", "`skew` | sample skewness (third moment) of the values\n", "`kurt` | sample kurtosis (fourth moment) of the values\n", "`cumsum` | cumulative sum of the values\n", "`cummin`, `cummax` | cumulated minimum and maximum of the values respectively\n", "`cumprod` | cumulated product of the values\n", "`diff` | calculation of the first arithmetic difference (useful for time series)\n", "`pct_change` | calculation of the percentage changes" ] }, { "cell_type": "markdown", "id": "317bdb46", "metadata": {}, "source": [ "## `ydata-profiling`\n", "\n", "[ydata-profiling](https://docs.profiling.ydata.ai/latest/) generates profile reports from a pandas DataFrame. The pandas `df.describe()` function is handy, but a bit basic for exploratory data analysis. ydata-profiling extends pandas DataFrame with `df.profile_report()`, which automatically generates a standardised report for understanding the data." ] }, { "cell_type": "markdown", "id": "5cde7e28", "metadata": {}, "source": [ "### Installation\n", "\n", "```bash\n", "$ uv add standard-imghdr legacy-cgi \"ydata-profiling[notebook, unicode]\"\n", "Resolved 251 packages in 2.53s\n", "Prepared 1 package in 106ms\n", "Installed 24 packages in 155ms\n", " + annotated-types==0.7.0\n", " + dacite==1.9.2\n", " + htmlmin==0.1.12\n", " + imagehash==4.3.1\n", " + legacy-cgi==2.6.3\n", " + llvmlite==0.44.0\n", " + multimethod==1.12\n", " + networkx==3.5\n", " + numba==0.61.0\n", " + patsy==1.0.1\n", " + phik==0.12.4\n", " + puremagic==1.29\n", " + pydantic==2.11.7\n", " + pydantic-core==2.33.2\n", " + pywavelets==1.8.0\n", " + seaborn==0.13.2\n", " + standard-imghdr==3.13.0\n", " + statsmodels==0.14.4\n", " + tangled-up-in-unicode==0.2.0\n", " + typeguard==4.4.2\n", " + typing-inspection==0.4.1\n", " + visions==0.8.1\n", " + wordcloud==1.9.4\n", " + ydata-profiling==4.16.1\n", "$ uv run jupyter notebook\n", "```\n", "\n", "In Python 3.13, the `imghdr` and `cgi` modules were removed, see also [PEP 594](https://peps.python.org/pep-0594/). However, as a workaround for these legacy products, [standard-imghdr](https://pypi.org/project/standard-imghdr/) and [legacy-cgi](https://pypi.org/project/legacy-cgi/) were provided in the Python Package Index." ] }, { "cell_type": "markdown", "id": "9da36ef5", "metadata": {}, "source": [ "### Example" ] }, { "cell_type": "code", "execution_count": 10, "id": "d51c33c4-8a3c-49cf-badf-8cb61ea6cdfb", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:35.940924Z", "iopub.status.busy": "2026-05-21T12:53:35.940863Z", "iopub.status.idle": "2026-05-21T12:53:38.305021Z", "shell.execute_reply": "2026-05-21T12:53:38.304633Z", "shell.execute_reply.started": "2026-05-21T12:53:35.940917Z" } }, "outputs": [ { "data": { "text/html": [ "\n", "
\n", " Upgrade to ydata-sdk\n", "

\n", " Improve your data and profiling with ydata-sdk, featuring data quality scoring, redundancy detection, outlier identification, text validation, and synthetic data generation.\n", "

\n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bf3ec8d89669441db03eb83dcbcd310e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from ydata_profiling import ProfileReport\n", "\n", "\n", "profile = ProfileReport(df2, title=\"pandas Profiling Report\")\n", "\n", "profile.to_notebook_iframe()" ] }, { "cell_type": "markdown", "id": "7081a6dc", "metadata": {}, "source": [ "### Configuration for large datasets\n", "\n", "By default, ydata-profiling summarises the dataset to provide the most insights for data analysis. If the computation time of profiling becomes a bottleneck, pandas-profiling offers several alternatives to overcome it. For the following examples, we first read a larger data set into pandas:" ] }, { "cell_type": "code", "execution_count": 11, "id": "0a4050bd", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:38.305883Z", "iopub.status.busy": "2026-05-21T12:53:38.305644Z", "iopub.status.idle": "2026-05-21T12:53:38.493593Z", "shell.execute_reply": "2026-05-21T12:53:38.493253Z", "shell.execute_reply.started": "2026-05-21T12:53:38.305871Z" } }, "outputs": [], "source": [ "titanic = pd.read_csv(\n", " \"https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\",\n", ")" ] }, { "cell_type": "markdown", "id": "d2c2f9f9", "metadata": {}, "source": [ "#### 1. minimal mode\n", "\n", "ydata-profiling contains a minimal configuration file config_minimal.yaml, in which the most expensive calculations are turned off by default. This is the recommended starting point for larger data sets." ] }, { "cell_type": "code", "execution_count": 12, "id": "3be9d8c1", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:38.494278Z", "iopub.status.busy": "2026-05-21T12:53:38.494050Z", "iopub.status.idle": "2026-05-21T12:53:41.061397Z", "shell.execute_reply": "2026-05-21T12:53:41.060982Z", "shell.execute_reply.started": "2026-05-21T12:53:38.494237Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4d6c4f6e0ffe44a5a83808d72aacc606", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "profile = ProfileReport(\n", " titanic,\n", " title=\"Minimal pandas Profiling Report\",\n", " minimal=True,\n", ")\n", "\n", "profile.to_notebook_iframe()" ] }, { "cell_type": "markdown", "id": "32d8b6b1", "metadata": {}, "source": [ "Further details on settings and configuration can be found in [Available settings](https://docs.profiling.ydata.ai/latest/advanced_settings/available_settings/#available-settings)." ] }, { "cell_type": "markdown", "id": "0bfe003a", "metadata": {}, "source": [ "#### 2. Sample\n", "An alternative option for very large data sets is to use only a part of them for the profiling report:" ] }, { "cell_type": "code", "execution_count": 13, "id": "3810cdcd", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:41.062065Z", "iopub.status.busy": "2026-05-21T12:53:41.061957Z", "iopub.status.idle": "2026-05-21T12:53:43.602057Z", "shell.execute_reply": "2026-05-21T12:53:43.601625Z", "shell.execute_reply.started": "2026-05-21T12:53:41.062056Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1e399052de744ff1af11a3a191a3fae2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sample = titanic.sample(frac=0.05)\n", "\n", "profile = ProfileReport(sample, title=\"Sample pandas Profiling Report\")\n", "\n", "profile.to_notebook_iframe()" ] }, { "cell_type": "markdown", "id": "e80b2c1d", "metadata": {}, "source": [ "#### 3. Deactivate expensive calculations\n", "\n", "To reduce the computational effort in large datasets, but still get some interesting information, some calculations can be filtered only for certain columns:" ] }, { "cell_type": "code", "execution_count": 14, "id": "cd2e2108", "metadata": { "execution": { "iopub.execute_input": "2026-05-21T12:53:43.602765Z", "iopub.status.busy": "2026-05-21T12:53:43.602638Z", "iopub.status.idle": "2026-05-21T12:53:46.093191Z", "shell.execute_reply": "2026-05-21T12:53:46.092812Z", "shell.execute_reply.started": "2026-05-21T12:53:43.602752Z" } }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f92411f6b0cf435391b1e549404e6615", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "profile = ProfileReport()\n", "profile.config.interactions.targets = [\"Sex\", \"Age\"]\n", "profile.df = titanic\n", "\n", "profile.to_notebook_iframe()" ] }, { "cell_type": "markdown", "id": "79de51e7", "metadata": {}, "source": [ "The setting `interactions.targets`, can be changed via configuration files as well as via environment variables; see [Interactions](https://docs.profiling.ydata.ai/latest/advanced_settings/available_settings/#interactions) for details." ] }, { "cell_type": "markdown", "id": "99e633e1", "metadata": {}, "source": [ "#### 4 Concurrency\n", "\n", "Currently work is being done on a scalable Spark backend for pandas-profiling, see [Spark profiling support](https://github.com/orgs/ydataai/projects/16/views/2)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.13 Kernel", "language": "python", "name": "python313" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.0" }, "widgets": 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