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processing, CSV file I/O (e.g. pd.read_csv)\n", + "\n", + "import plotly.express as px\n", + "import plotly.io as pio\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "import seaborn as sns\n", + "import matplotlib\n", + "from matplotlib import pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "pd.pandas.set_option('display.max_columns',None)\n", + "pd.pandas.set_option('display.max_rows',None)\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. What is the general outlook and statistics of the data?\n", + "The data you've linked to is an insurance dataset that contains information about insurance customers, including their age, gender, bmi (body mass index), children, smoker status, region, and charges for insurance.\n", + "* a. Data types\n", + "Ans: There are integer and float number and object types\n", + "* b. Number of rows and columns\n", + "Ans: There are 1337 rows and 7 columns\n", + "* c. Description of attributes\n", + "Ans: There are categorical value for examples; sex, region, smoker. \n", + "* d. Statistical outlook\n", + "Ans: Data looks normal as statiscal for example childeren min 0 ad max 5. That is quite normal.\n", + "But charges differs huge. We should look into why there is so much difference. There are continues and integer number as well\n", + "* e. NaN values\n", + "Ans: There is no missing number." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv('insurance.csv')\n", + "df = data.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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333male22.7050nonorthwest21984.47061
432male28.8800nonorthwest3866.85520
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" + ], + "text/plain": [ + " age sex bmi children smoker region charges\n", + "0 19 female 27.900 0 yes southwest 16884.92400\n", + "1 18 male 33.770 1 no southeast 1725.55230\n", + "2 28 male 33.000 3 no southeast 4449.46200\n", + "3 33 male 22.705 0 no northwest 21984.47061\n", + "4 32 male 28.880 0 no northwest 3866.85520" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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children1338.01.0949181.2054930.00000.000001.0002.0000005.00000
charges1338.013270.42226512110.0112371121.87394740.287159382.03316639.91251563770.42801
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" + ], + "text/plain": [ + " count mean std min 25% 50% \\\n", + "age 1338.0 39.207025 14.049960 18.0000 27.00000 39.000 \n", + "bmi 1338.0 30.663397 6.098187 15.9600 26.29625 30.400 \n", + "children 1338.0 1.094918 1.205493 0.0000 0.00000 1.000 \n", + "charges 1338.0 13270.422265 12110.011237 1121.8739 4740.28715 9382.033 \n", + "\n", + " 75% max \n", + "age 51.000000 64.00000 \n", + "bmi 34.693750 53.13000 \n", + "children 2.000000 5.00000 \n", + "charges 16639.912515 63770.42801 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe().T" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.info" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age int64\n", + "sex object\n", + "bmi float64\n", + "children int64\n", + "smoker object\n", + "region object\n", + "charges float64\n", + "dtype: object" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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sex2
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children6
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region4
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" + ], + "text/plain": [ + " No. of unique values\n", + "age 47\n", + "sex 2\n", + "bmi 548\n", + "children 6\n", + "smoker 2\n", + "region 4\n", + "charges 1337" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unique_val = []\n", + "for i in df.columns:\n", + " u = df[i].nunique()\n", + " unique_val.append(u)\n", + " \n", + "pd.DataFrame({\"No. of unique values\": unique_val}, index=df.columns)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "age 0\n", + "sex 0\n", + "bmi 0\n", + "children 0\n", + "smoker 0\n", + "region 0\n", + "charges 0\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. What is the frequency of categorical variables? Is there any significant difference in frequencies?\n", + "\n", + "Tip: You can use bar chart and pie chart with subplots." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "alignmentgroup": "True", + "bingroup": "x", + "hovertemplate": "sex=%{x}
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