{ "cells": [ { "cell_type": "markdown", "id": "92f5ec54", "metadata": {}, "source": [ "## Creating a Series" ] }, { "cell_type": "code", "execution_count": 1, "id": "06e8f3f1", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "id": "f94e0c4d", "metadata": {}, "source": [ "### Empty Series" ] }, { "cell_type": "code", "execution_count": 2, "id": "6159522c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Series([], dtype: float64)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "<ipython-input-2-ef047fdb75c6>:1: DeprecationWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n", " a=pd.Series()\n" ] } ], "source": [ "a=pd.Series()\n", "print(a)" ] }, { "cell_type": "markdown", "id": "bf353326", "metadata": {}, "source": [ "### From List" ] }, { "cell_type": "code", "execution_count": 6, "id": "522a6c18", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 A\n", "1 B\n", "2 C\n", "dtype: object\n", "0 A\n", "1 B\n", "2 C\n", "dtype: object\n" ] } ], "source": [ "list1=['A','B','C']\n", "print(pd.Series(list1))\n", "\n", "# or\n", "\n", "print(pd.Series(['A','B','C']))" ] }, { "cell_type": "markdown", "id": "e2c21024", "metadata": {}, "source": [ "#### Custom indexes" ] }, { "cell_type": "code", "execution_count": 13, "id": "cd0e32d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "First A\n", "Second B\n", "Third C\n", "Fourth D\n", "dtype: object\n" ] } ], "source": [ "a=pd.Series(['A','B','C','D'],index=['First','Second','Third','Fourth'])\n", "print(a)" ] }, { "cell_type": "markdown", "id": "d8cdb619", "metadata": {}, "source": [ "### From numpy array" ] }, { "cell_type": "code", "execution_count": 15, "id": "3e1b2dab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 S\n", "1 A\n", "2 H\n", "3 I\n", "4 L\n", "dtype: object\n" ] } ], "source": [ "# simple array\n", "data = np.array(['S','A','H','I','L'])\n", "a = pd.Series(data)\n", "print(a)" ] }, { "cell_type": "markdown", "id": "73b1a8d6", "metadata": {}, "source": [ "### From dictionary" ] }, { "cell_type": "code", "execution_count": 17, "id": "4ba266f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0 Sahil\n", "1 Sonia\n", "2 Sourav\n", "dtype: object\n" ] } ], "source": [ "b=pd.Series({\n", " 0:'Sahil',\n", " 1:'Sonia',\n", " 2:'Sourav'\n", "})\n", "print(b)" ] }, { "cell_type": "markdown", "id": "a7d6a030", "metadata": {}, "source": [ "### Assigning indexes after declaring the series" ] }, { "cell_type": "code", "execution_count": 19, "id": "01a649d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a Sahil\n", "b Sonia\n", "c Sourav\n", "dtype: object\n" ] } ], "source": [ "new_indexes=['a','b','c']\n", "b.index=new_indexes\n", "print(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": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 5 }