{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Visualization can be done by implementing Python libraries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import different libraries like pandas, numpy, seaborn, matplotlib" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Read data from the csv file" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "data = pd.read_csv(\"heart_failure_clinical_records_dataset.csv\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | age | \n", "anaemia | \n", "creatinine_phosphokinase | \n", "diabetes | \n", "ejection_fraction | \n", "high_blood_pressure | \n", "platelets | \n", "serum_creatinine | \n", "serum_sodium | \n", "sex | \n", "smoking | \n", "time | \n", "DEATH_EVENT | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "75.0 | \n", "0 | \n", "582 | \n", "0 | \n", "20 | \n", "1 | \n", "265000.00 | \n", "1.9 | \n", "130 | \n", "1 | \n", "0 | \n", "4 | \n", "1 | \n", "
1 | \n", "55.0 | \n", "0 | \n", "7861 | \n", "0 | \n", "38 | \n", "0 | \n", "263358.03 | \n", "1.1 | \n", "136 | \n", "1 | \n", "0 | \n", "6 | \n", "1 | \n", "
2 | \n", "65.0 | \n", "0 | \n", "146 | \n", "0 | \n", "20 | \n", "0 | \n", "162000.00 | \n", "1.3 | \n", "129 | \n", "1 | \n", "1 | \n", "7 | \n", "1 | \n", "
3 | \n", "50.0 | \n", "1 | \n", "111 | \n", "0 | \n", "20 | \n", "0 | \n", "210000.00 | \n", "1.9 | \n", "137 | \n", "1 | \n", "0 | \n", "7 | \n", "1 | \n", "
4 | \n", "65.0 | \n", "1 | \n", "160 | \n", "1 | \n", "20 | \n", "0 | \n", "327000.00 | \n", "2.7 | \n", "116 | \n", "0 | \n", "0 | \n", "8 | \n", "1 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
294 | \n", "62.0 | \n", "0 | \n", "61 | \n", "1 | \n", "38 | \n", "1 | \n", "155000.00 | \n", "1.1 | \n", "143 | \n", "1 | \n", "1 | \n", "270 | \n", "0 | \n", "
295 | \n", "55.0 | \n", "0 | \n", "1820 | \n", "0 | \n", "38 | \n", "0 | \n", "270000.00 | \n", "1.2 | \n", "139 | \n", "0 | \n", "0 | \n", "271 | \n", "0 | \n", "
296 | \n", "45.0 | \n", "0 | \n", "2060 | \n", "1 | \n", "60 | \n", "0 | \n", "742000.00 | \n", "0.8 | \n", "138 | \n", "0 | \n", "0 | \n", "278 | \n", "0 | \n", "
297 | \n", "45.0 | \n", "0 | \n", "2413 | \n", "0 | \n", "38 | \n", "0 | \n", "140000.00 | \n", "1.4 | \n", "140 | \n", "1 | \n", "1 | \n", "280 | \n", "0 | \n", "
298 | \n", "50.0 | \n", "0 | \n", "196 | \n", "0 | \n", "45 | \n", "0 | \n", "395000.00 | \n", "1.6 | \n", "136 | \n", "1 | \n", "1 | \n", "285 | \n", "0 | \n", "
299 rows × 13 columns
\n", "