diff --git a/data_cleaning_using_numpy_and_pandas.ipynb b/data_cleaning_using_numpy_and_pandas.ipynb new file mode 100644 index 0000000000000..f6280bac41c76 --- /dev/null +++ b/data_cleaning_using_numpy_and_pandas.ipynb @@ -0,0 +1,4320 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMgQy7SQ2SxtbxPZQQwGG/Q", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "V1wBFlZtCTEq" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "source": [ + "df = pd.read_csv(\"/content/Data-cleaning-for-beginners-using-pandas.csv\",index_col=0)" + ], + "metadata": { + "id": "-nhyZMBWtjqL" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 237 + }, + "id": "9v5VHadhubdc", + "outputId": "4363d5f9-ad8a-4248-cf75-5212896f3ce2" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Age Salary Rating Location Established Easy Apply\n", + "Index \n", + "0 44.0 $44k-$99k 5.4 India,In 1999 TRUE\n", + "1 66.0 $55k-$66k 3.5 New York,Ny 2002 TRUE\n", + "2 NaN $77k-$89k -1.0 New York,Ny -1 -1\n", + "3 64.0 $44k-$99k 4.4 India In 1988 -1\n", + "4 25.0 $44k-$99k 6.4 Australia Aus 2002 -1" + ], + "text/html": [ + "\n", + "
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Index
044.0$44k-$99k5.4India,In1999TRUE
166.0$55k-$66k3.5New York,Ny2002TRUE
239.0$77k-$89k-1.0New York,Ny-1-1
364.0$44k-$99k4.4India In1988-1
425.0$44k-$99k6.4Australia Aus2002-1
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pandas.core.generic.NDFrame.astype
def astype(dtype, copy: bool_t | None=None, errors: IgnoreRaise='raise') -> NDFrameT
/usr/local/lib/python3.10/dist-packages/pandas/core/generic.pyCast a pandas object to a specified dtype ``dtype``.\n",
+              "\n",
+              "Parameters\n",
+              "----------\n",
+              "dtype : str, data type, Series or Mapping of column name -> data type\n",
+              "    Use a str, numpy.dtype, pandas.ExtensionDtype or Python type to\n",
+              "    cast entire pandas object to the same type. Alternatively, use a\n",
+              "    mapping, e.g. {col: dtype, ...}, where col is a column label and dtype is\n",
+              "    a numpy.dtype or Python type to cast one or more of the DataFrame's\n",
+              "    columns to column-specific types.\n",
+              "copy : bool, default True\n",
+              "    Return a copy when ``copy=True`` (be very careful setting\n",
+              "    ``copy=False`` as changes to values then may propagate to other\n",
+              "    pandas objects).\n",
+              "errors : {'raise', 'ignore'}, default 'raise'\n",
+              "    Control raising of exceptions on invalid data for provided dtype.\n",
+              "\n",
+              "    - ``raise`` : allow exceptions to be raised\n",
+              "    - ``ignore`` : suppress exceptions. On error return original object.\n",
+              "\n",
+              "Returns\n",
+              "-------\n",
+              "same type as caller\n",
+              "\n",
+              "See Also\n",
+              "--------\n",
+              "to_datetime : Convert argument to datetime.\n",
+              "to_timedelta : Convert argument to timedelta.\n",
+              "to_numeric : Convert argument to a numeric type.\n",
+              "numpy.ndarray.astype : Cast a numpy array to a specified type.\n",
+              "\n",
+              "Notes\n",
+              "-----\n",
+              ".. versionchanged:: 2.0.0\n",
+              "\n",
+              "    Using ``astype`` to convert from timezone-naive dtype to\n",
+              "    timezone-aware dtype will raise an exception.\n",
+              "    Use :meth:`Series.dt.tz_localize` instead.\n",
+              "\n",
+              "Examples\n",
+              "--------\n",
+              "Create a DataFrame:\n",
+              "\n",
+              ">>> d = {'col1': [1, 2], 'col2': [3, 4]}\n",
+              ">>> df = pd.DataFrame(data=d)\n",
+              ">>> df.dtypes\n",
+              "col1    int64\n",
+              "col2    int64\n",
+              "dtype: object\n",
+              "\n",
+              "Cast all columns to int32:\n",
+              "\n",
+              ">>> df.astype('int32').dtypes\n",
+              "col1    int32\n",
+              "col2    int32\n",
+              "dtype: object\n",
+              "\n",
+              "Cast col1 to int32 using a dictionary:\n",
+              "\n",
+              ">>> df.astype({'col1': 'int32'}).dtypes\n",
+              "col1    int32\n",
+              "col2    int64\n",
+              "dtype: object\n",
+              "\n",
+              "Create a series:\n",
+              "\n",
+              ">>> ser = pd.Series([1, 2], dtype='int32')\n",
+              ">>> ser\n",
+              "0    1\n",
+              "1    2\n",
+              "dtype: int32\n",
+              ">>> ser.astype('int64')\n",
+              "0    1\n",
+              "1    2\n",
+              "dtype: int64\n",
+              "\n",
+              "Convert to categorical type:\n",
+              "\n",
+              ">>> ser.astype('category')\n",
+              "0    1\n",
+              "1    2\n",
+              "dtype: category\n",
+              "Categories (2, int32): [1, 2]\n",
+              "\n",
+              "Convert to ordered categorical type with custom ordering:\n",
+              "\n",
+              ">>> from pandas.api.types import CategoricalDtype\n",
+              ">>> cat_dtype = CategoricalDtype(\n",
+              "...     categories=[2, 1], ordered=True)\n",
+              ">>> ser.astype(cat_dtype)\n",
+              "0    1\n",
+              "1    2\n",
+              "dtype: category\n",
+              "Categories (2, int64): [2 < 1]\n",
+              "\n",
+              "Create a series of dates:\n",
+              "\n",
+              ">>> ser_date = pd.Series(pd.date_range('20200101', periods=3))\n",
+              ">>> ser_date\n",
+              "0   2020-01-01\n",
+              "1   2020-01-02\n",
+              "2   2020-01-03\n",
+              "dtype: datetime64[ns]
\n", + " \n", + "
" + ] + }, + "metadata": {}, + "execution_count": 15 + } + ] + }, + { + "cell_type": "code", + "source": [ + "df['location']= df['location'].str.replace(\"Australia Aus\", \"Australia, Aus\")\n", + "df['location_city'] = df['location'].str.split(\",\", expand=True).get(0)\n", + "df['city_sign'] = df['location'].str.split(\",\", expand=True).get(1)\n", + "df.drop('location', inplace=True, axis=1)" + ], + "metadata": { + "id": "DyKI3GTIxlfw" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df['rating'] = df[\"rating\"].replace(-1,np.nan)\n", + "df['rating'] = df[\"rating\"].fillna(df['rating'].mean()).round(decimals=1)" + ], + "metadata": { + "id": "0qS9KbwEx5G7" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df['easy_apply'] = df['easy_apply'].replace(\"-1\", \"False\")" + ], + "metadata": { + "id": "NzF89RvByHRj" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df['salary'].iloc[0]\n", + "\n", + "df['salary'] = df['salary'].apply(lambda x:x.replace(\"$\",''))\n", + "df['salary'] = df['salary'].apply(lambda x:x.replace(\"k\",''))" + ], + "metadata": { + "id": "yW1W8vvdyQCZ" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 990 + }, + "id": "AgKNGOGnyfhR", + "outputId": "421c7e78-01be-4142-ffc2-b81e086b5ae6" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " age salary rating established easy_apply location_city city_sign\n", + "Index \n", + "0 44.0 44-99 5.4 1999.0 TRUE India In\n", + "1 66.0 55-66 3.5 2002.0 TRUE New York Ny\n", + "2 39.0 77-89 4.3 Unknown False New York Ny\n", + "3 64.0 44-99 4.4 1988.0 False India In None\n", + "4 25.0 44-99 6.4 2002.0 False Australia Aus\n", + "5 44.0 77-89 1.4 1999.0 TRUE India In\n", + "6 21.0 44-99 0.0 Unknown False New York Ny\n", + "7 44.0 44-99 4.3 Unknown False Australia Aus\n", + "8 35.0 44-99 5.4 Unknown False New York Ny\n", + "9 22.0 44-99 7.7 Unknown TRUE India In\n", + "10 55.0 10-49 5.4 2008.0 TRUE India In\n", + "11 44.0 10-49 6.7 2009.0 False India In\n", + "12 39.0 44-99 0.0 1999.0 False India In\n", + "13 25.0 44-99 4.3 2019.0 TRUE Australia Aus\n", + "14 66.0 44-99 4.0 2020.0 TRUE Australia Aus\n", + "15 44.0 88-101 3.0 1999.0 False Australia Aus\n", + "16 19.0 19-40 4.5 1984.0 False India In\n", + "17 39.0 44-99 5.3 1943.0 TRUE New York Ny\n", + "18 35.0 44-99 6.7 1954.0 TRUE New York Ny\n", + "19 32.0 44-99 3.3 1955.0 TRUE New York Ny\n", + "20 39.0 44-99 5.7 1944.0 TRUE New York Ny\n", + "21 35.0 44-99 5.0 1946.0 False New York Ny\n", + "22 19.0 55-66 7.8 1988.0 TRUE New York Ny\n", + "23 39.0 44-99 2.4 1999.0 TRUE New York Ny\n", + "24 13.0 44-99 4.3 1987.0 False New York Ny\n", + "25 55.0 44-99 0.0 1980.0 TRUE Australia Aus\n", + "26 39.0 55-66 4.3 1934.0 TRUE India In\n", + "27 52.0 44-99 5.4 1935.0 False India In\n", + "28 39.0 39-88 3.4 1932.0 False Australia Aus" + ], + "text/html": [ + "\n", + "
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agesalaryratingestablishedeasy_applylocation_citycity_sign
Index
044.044-995.41999.0TRUEIndiaIn
166.055-663.52002.0TRUENew YorkNy
239.077-894.3UnknownFalseNew YorkNy
364.044-994.41988.0FalseIndia InNone
425.044-996.42002.0FalseAustraliaAus
544.077-891.41999.0TRUEIndiaIn
621.044-990.0UnknownFalseNew YorkNy
744.044-994.3UnknownFalseAustraliaAus
835.044-995.4UnknownFalseNew YorkNy
922.044-997.7UnknownTRUEIndiaIn
1055.010-495.42008.0TRUEIndiaIn
1144.010-496.72009.0FalseIndiaIn
1239.044-990.01999.0FalseIndiaIn
1325.044-994.32019.0TRUEAustraliaAus
1466.044-994.02020.0TRUEAustraliaAus
1544.088-1013.01999.0FalseAustraliaAus
1619.019-404.51984.0FalseIndiaIn
1739.044-995.31943.0TRUENew YorkNy
1835.044-996.71954.0TRUENew YorkNy
1932.044-993.31955.0TRUENew YorkNy
2039.044-995.71944.0TRUENew YorkNy
2135.044-995.01946.0FalseNew YorkNy
2219.055-667.81988.0TRUENew YorkNy
2339.044-992.41999.0TRUENew YorkNy
2413.044-994.31987.0FalseNew YorkNy
2555.044-990.01980.0TRUEAustraliaAus
2639.055-664.31934.0TRUEIndiaIn
2752.044-995.41935.0FalseIndiaIn
2839.039-883.41932.0FalseAustraliaAus
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111Izze[Clementine]$3.39
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\n" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "dataframe", + "variable_name": "df", + "summary": "{\n \"name\": \"df\",\n \"rows\": 4622,\n \"fields\": [\n {\n \"column\": \"order_id\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 528,\n \"min\": 1,\n \"max\": 1834,\n \"num_unique_values\": 1834,\n \"samples\": [\n 644,\n 333,\n 991\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"quantity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 1,\n \"max\": 15,\n \"num_unique_values\": 9,\n \"samples\": [\n 8,\n 2,\n 15\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"item_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 50,\n \"samples\": [\n \"Barbacoa Burrito\",\n \"Crispy Tacos\",\n \"Chips and Roasted Chili Corn Salsa\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"choice_description\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1043,\n \"samples\": [\n \"[Roasted Chili Corn Salsa, [Black Beans, Sour Cream, Cheese, Guacamole]]\",\n \"[Tomatillo Red Chili Salsa, [Guacamole, Cheese]]\",\n \"[Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]]\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"item_price\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 4.245557197940302,\n \"min\": 1.09,\n \"max\": 44.25,\n \"num_unique_values\": 78,\n \"samples\": [\n 23.5,\n 2.39,\n 7.4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 43 + } + ] + }, + { + "cell_type": "code", + "source": [ + "revene= df['item_price'].sum()\n", + "print('Revenue was: $',revene)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "yXVc_PpF2moL", + "outputId": "5ae57e9e-ddeb-438c-b2ff-4488da2d86b4" + }, + "execution_count": 44, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Revenue was: $ 34500.16\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "orders = df.order_id.value_counts().count()\n", + "orders" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "66wd1ZXQ2pcU", + "outputId": "d8d1ed51-e78c-473c-fee7-9105597cdbf3" + }, + "execution_count": 45, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "1834" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Convert 'item_price' to string before using .str accessor\n", + "df[\"item_price\"] = df[\"item_price\"].astype(str).str.replace('[^\\d.]', '', regex=True).astype(float)\n", + "\n", + "# Calculate revenue for each item\n", + "df['revenue'] = df['quantity'] * df['item_price']\n", + "\n", + "# Group by order_id and calculate total revenue per order\n", + "order_grouped = df.groupby(by=['order_id']).sum()\n", + "\n", + "# Calculate the mean revenue per order\n", + "mean_revenue = order_grouped['revenue'].mean()\n", + "print(mean_revenue)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "r4AVZ_-D2-TD", + "outputId": "2e109bde-8582-4531-eb3c-447caaa456c4" + }, + "execution_count": 50, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "21.39423118865867\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "df.item_name.value_counts().count()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ICBq8jNz4QtY", + "outputId": "67406211-7361-4a0c-c70a-c437dd82ec52" + }, + "execution_count": 51, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "50" + ] + }, + "metadata": {}, + "execution_count": 51 + } + ] + } + ] +} \ No newline at end of file