{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create and view an object\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\nimport numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Create an object**\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "series = pd.Series([1, 3, 5, np.nan, 6, 8])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dates = pd.date_range(\"20220501\", periods=6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame = pd.DataFrame(np.random.randn(6, 4), index=dates, columns=list(\"ABCD\"))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame2 = pd.DataFrame(\n {\n \"A\": 1.0,\n \"B\": pd.Timestamp(\"20220501\"),\n \"C\": pd.Series(1, index=list(range(4)), dtype=\"float32\"),\n \"D\": np.array([3] * 4, dtype=\"int32\"),\n \"E\": pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n \"F\": \"foo\",\n }\n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**View an object**\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame2.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame2.tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame2.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame2.index" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame2.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame2.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Convert to numpy\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame.to_numpy()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Transpose, sorting data**\n\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame.T" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame.sort_index(axis=1, ascending=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dataFrame.sort_values(by=\"B\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 0 }