{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append(\"..\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from optimus import Optimus" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "op = Optimus(\"vaex\")" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "import polars as pl\n", "\n", "q = (\n", " pl.scan_parquet(\"../../Data input/Base Pre2Pos para Modelo - Abr 22.parquet\") \n", ")\n", "\n", "df = q.collect()" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "shape: (163820, 78)\n", "┌────────┬───────────┬──────┬────────┬─────┬───────────────┬──────────┬────────┬───────────────┐\n", "│ NO ┆ CO_ID ┆ ARPU ┆ TENURE ┆ ... ┆ TELEFONO_FIJO ┆ INTERNET ┆ MBB ┆ MOVIL_POSPAGO │\n", "│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n", "│ i64 ┆ i64 ┆ i64 ┆ i64 ┆ ┆ str ┆ str ┆ str ┆ str │\n", "╞════════╪═══════════╪══════╪════════╪═════╪═══════════════╪══════════╪════════╪═══════════════╡\n", "│ 1 ┆ 12881892 ┆ 384 ┆ 109 ┆ ... ┆ $null$ ┆ $null$ ┆ $null$ ┆ $null$ │\n", "├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n", "│ 2 ┆ 136449998 ┆ 410 ┆ 32 ┆ ... ┆ $null$ ┆ $null$ ┆ 1 ┆ $null$ │\n", "├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n", "│ 3 ┆ 15661796 ┆ 654 ┆ 93 ┆ ... ┆ $null$ ┆ $null$ ┆ $null$ ┆ $null$ │\n", "├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n", "│ 4 ┆ 127068927 ┆ 350 ┆ 37 ┆ ... ┆ $null$ ┆ $null$ ┆ $null$ ┆ $null$ │\n", "├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n", "│ ... ┆ ... ┆ ... ┆ ... ┆ ... ┆ ... ┆ ... ┆ ... ┆ ... │\n", "├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n", "│ 163817 ┆ 2581628 ┆ 732 ┆ 190 ┆ ... ┆ 1 ┆ $null$ ┆ $null$ ┆ $null$ │\n", "├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n", "│ 163818 ┆ 96685857 ┆ 641 ┆ 53 ┆ ... ┆ $null$ ┆ $null$ ┆ $null$ ┆ $null$ │\n", "├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n", "│ 163819 ┆ 157842933 ┆ 624 ┆ 8 ┆ ... ┆ $null$ ┆ $null$ ┆ $null$ ┆ $null$ │\n", "├╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌╌╌╌┤\n", "│ 163820 ┆ 17487527 ┆ 623 ┆ 82 ┆ ... ┆ $null$ ┆ $null$ ┆ $null$ ┆ 1 │\n", "└────────┴───────────┴──────┴────────┴─────┴───────────────┴──────────┴────────┴───────────────┘" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 19.2 ms\n" ] }, { "data": { "text/plain": [ "(163820, 163820)" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "(df[\"NO\"].cast(str).str.contains(r\"^\\d+$\").sum(), df[\"CO_ID\"].cast(str).str.contains(r\"^\\d+$\").sum())" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 2.22 s\n" ] } ], "source": [ "%%time\n", "# df =op.load.file(\"data/crime.csv\")\n", "df =op.load.file(\"../../Data input/Base Pre2Pos para Modelo - Abr 22.parquet\")" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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None partition(s) <class 'optimus.engines.vaex.dataframe.VaexDataFrame'>
\n", "\n" ], "text/plain": [ " NO\n", " (bool)\n", "--------\n", " 1\n", " 1\n", " 1\n", " 1\n", " 1\n", " 1\n", " 1\n", " 1\n", " 1\n", " 1\n", " 1" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"NO\"].mask.int()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NO\n", "Wall time: 1.43 s\n", "CO_ID\n", "Wall time: 1.63 s\n", "ARPU\n", "Wall time: 1.51 s\n", "TENURE\n", "Wall time: 1.61 s\n", "FECHA_INST_TELEFONO\n", "Wall time: 1.68 s\n", "FECHA_INSTALACION\n", "Wall time: 1.58 s\n", "STATUS_TELEFONO\n", "Wall time: 1.66 s\n", "CANAL_VENTA\n", "Wall time: 1.71 s\n", "SUB_CANAL_VENTA\n", "Wall time: 2.37 s\n", "ID_VENDEDOR\n", "Wall time: 1.87 s\n", "ID_PLAN\n", "Wall time: 2.15 s\n", "PLAN_PREPAGO\n", "Wall time: 1.84 s\n", "ID_PLAN_ANTERIOR\n", "Wall time: 1.67 s\n", "FECHA_CAMBIO_PLAN\n", "Wall time: 1.71 s\n", "TRA_IN_LOC_Mean_6_MESES\n", "Wall time: 1.74 s\n", "TRA_IN_INT_Mean_6_MESES\n", "Wall time: 1.67 s\n", "TRA_IN_NAT_Mean_6_MESES\n", "Wall time: 1.69 s\n", "TRA_IN_MOBILE_Mean_6_MESES\n", "Wall time: 2.19 s\n", "TRA_IN_MOBILE_OTHER_Mean_6_MESES\n", "Wall time: 1.66 s\n", "TRA_OUT_LOC_Mean_6_MESES\n", "Wall time: 1.56 s\n", "TRA_OUT_INT_Mean_6_MESES\n", "Wall time: 1.54 s\n", "TRA_OUT_NAT_Mean_6_MESES\n", "Wall time: 1.56 s\n", "TRA_OUT_MOBILE_Mean_6_MESES\n", "Wall time: 1.51 s\n", "TRA_OUT_MOBILE_OTHER_Mean_6_MESES\n", "Wall time: 1.57 s\n", "SMS_OUT_AMT_Mean_6_MESES\n", "Wall time: 1.74 s\n", "RECHARGE_AMT_Mean_6_MESES\n", "Wall time: 1.98 s\n", "AMT_RENT_Mean_6_MESES\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mprofile\u001b[1;34m(self, cols, bins, flush)\u001b[0m\n\u001b[0;32m 827\u001b[0m \"\"\"\n\u001b[0;32m 828\u001b[0m \u001b[1;31m# Uses profile on self instead of calculate_profile to get the data only when it's necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 829\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflush\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 830\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m 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53\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mspec\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 54\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdeepcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmeta\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m \u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mspec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 56\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 57\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 345\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\glom\\mutation.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, path, ignore_missing)\u001b[0m\n\u001b[0;32m 261\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mignore_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 262\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbasestring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 263\u001b[1;33m \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrom_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 264\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mTType\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 265\u001b[0m \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\glom\\core.py\u001b[0m in \u001b[0;36mfrom_text\u001b[1;34m(cls, text)\u001b[0m\n\u001b[0;32m 637\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 638\u001b[0m \"\"\"\n\u001b[1;32m--> 639\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mtext\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_CACHE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 640\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_CACHE\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_MAX_CACHE\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "name": "stdout", "output_type": "stream", "text": [ "AMT_MIN_OUT_INT_Mean_6_MESES\n", "Wall time: 1.88 s\n", "AMT_MIN_OUT_MOBILE_OTHER_Mean_6_MESES\n", "Wall time: 1.82 s\n", "AMT_MIN_OUT_OFF_NET_FIX_Mean_6_MESES\n", "Wall time: 1.7 s\n", "AMT_MIN_OUT_MOBILE_Mean_6_MESES\n", "Wall time: 1.63 s\n", "AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_6_MESES\n", "Wall time: 1.68 s\n", "AMT_ROAMING_SMS_Mean_6_MESES\n", "Wall time: 1.65 s\n", "AMT_ROAMING_DATA_Mean_6_MESES\n", "Wall time: 1.79 s\n", "AMT_ROAMING_VOICE_Mean_6_MESES\n", "Wall time: 1.63 s\n", "AMT_PAQUETICO_VOZ_Mean_6_MESES\n", "Wall time: 1.63 s\n", "AMT_PAQUETICO_DATA_Mean_6_MESES\n", "Wall time: 1.65 s\n", "AMT_MIN_OUT_ON_NET_FIX_Mean_6_MESES\n", "Wall time: 1.8 s\n", "MB_Mean_6_MESES\n", "Wall time: 1.85 s\n", "TRA_IN_LOC_Mean_3_MESES\n", "Wall time: 1.71 s\n", "TRA_IN_INT_Mean_3_MESES\n", "Wall time: 1.65 s\n", "TRA_IN_NAT_Mean_3_MESES\n", "Wall time: 1.54 s\n", "TRA_IN_MOBILE_Mean_3_MESES\n", "Wall time: 1.54 s\n", "TRA_IN_MOBILE_OTHER_Mean_3_MESES\n", "Wall time: 1.64 s\n", "TRA_OUT_LOC_Mean_3_MESES\n", "Wall time: 1.66 s\n", "TRA_OUT_INT_Mean_3_MESES\n", "Wall time: 1.57 s\n", "TRA_OUT_NAT_Mean_3_MESES\n", "Wall time: 1.71 s\n", "TRA_OUT_MOBILE_Mean_3_MESES\n", "Wall time: 2.42 s\n", "TRA_OUT_MOBILE_OTHER_Mean_3_MESES\n", "Wall time: 2.33 s\n", "SMS_OUT_AMT_Mean_3_MESES\n", "Wall time: 1.93 s\n", "RECHARGE_AMT_Mean_3_MESES\n", "Wall time: 1.6 s\n", "AMT_RENT_Mean_3_MESES\n", "Wall time: 1.59 s\n", "AMT_MIN_OUT_INT_Mean_3_MESES\n", "Wall time: 1.56 s\n", "AMT_MIN_OUT_MOBILE_OTHER_Mean_3_MESES\n", "Wall time: 1.63 s\n", "AMT_MIN_OUT_OFF_NET_FIX_Mean_3_MESES\n", "Wall time: 1.65 s\n", "AMT_MIN_OUT_MOBILE_Mean_3_MESES\n", "Wall time: 1.82 s\n", "AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_3_MESES\n", "Wall time: 1.68 s\n", "AMT_ROAMING_SMS_Mean_3_MESES\n", "Wall time: 1.67 s\n", "AMT_ROAMING_DATA_Mean_3_MESES\n", "Wall time: 1.56 s\n", "AMT_ROAMING_VOICE_Mean_3_MESES\n", "Wall time: 1.55 s\n", "AMT_PAQUETICO_VOZ_Mean_3_MESES\n", "Wall time: 1.55 s\n", "AMT_PAQUETICO_DATA_Mean_3_MESES\n", "Wall time: 1.69 s\n", "AMT_MIN_OUT_ON_NET_FIX_Mean_3_MESES\n", "Wall time: 1.73 s\n", "MB_Mean_3_MESES\n", "Wall time: 1.56 s\n", "FECHA_NACIMIENTO\n", "Wall time: 1.69 s\n", "SEXO\n", "Wall time: 1.93 s\n", "SEGMENTO_BI\n", "Wall time: 1.84 s\n", "DESCRIPCION_PROVINCIA_CUENTA\n", "Wall time: 1.67 s\n", "DESCRIPCION_PROVINCIA_CVOZ\n", "Wall time: 2.02 s\n", "DESCRIPCION_PROVINCIA_CDATA\n", "Wall time: 1.61 s\n", "PROVINCIA\n", "Wall time: 1.58 s\n", "MONTO_RECARGA_Sum_MES_ANTERIOR\n", "Wall time: 1.65 s\n", "CANT_RECARGAS_MES_ANTERIOR\n", "Wall time: 1.6 s\n", "CABLE\n", "Wall time: 1.6 s\n", "TELEFONO_FIJO\n", "Wall time: 1.77 s\n", "INTERNET\n", "Wall time: 1.75 s\n", "MBB\n", "Wall time: 1.69 s\n", "MOVIL_POSPAGO\n", "Wall time: 1.71 s\n" ] } ], "source": [ "for col_name in df.cols.names(): \n", " print(col_name)\n", " %prun df.cols.profile(col_name)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " " ] } ], "source": [ "col_name = \"NO\"\n", "%prun df[col_name].cols.profile(col_name)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " " ] } ], "source": [ "%prun df.data[\"NO\"].astype(str).str.match(r\"^\\d+$\").sum()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NO\n", " CO_ID\n", " ARPU\n", " TENURE\n", " FECHA_INST_TELEFONO\n", " FECHA_INSTALACION\n", " STATUS_TELEFONO\n", " CANAL_VENTA\n", " SUB_CANAL_VENTA\n", " ID_VENDEDOR\n", " ID_PLAN\n", " PLAN_PREPAGO\n", " ID_PLAN_ANTERIOR\n", " FECHA_CAMBIO_PLAN\n", " TRA_IN_LOC_Mean_6_MESES\n", " TRA_IN_INT_Mean_6_MESES\n", " TRA_IN_NAT_Mean_6_MESES\n", " TRA_IN_MOBILE_Mean_6_MESES\n", " TRA_IN_MOBILE_OTHER_Mean_6_MESES\n", " TRA_OUT_LOC_Mean_6_MESES\n", " TRA_OUT_INT_Mean_6_MESES\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32mC:\\Users\\ARGENI~1\\AppData\\Local\\Temp/ipykernel_19916/1976845657.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol_name\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcol_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'prun'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'df[col_name].cols.quality(col_name)'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\IPython\\core\\interactiveshell.py\u001b[0m in \u001b[0;36mrun_line_magic\u001b[1;34m(self, magic_name, line, _stack_depth)\u001b[0m\n\u001b[0;32m 2349\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'local_ns'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_local_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstack_depth\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2350\u001b[0m \u001b[1;32mwith\u001b[0m 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"\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\vaex\\multithreading.py\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, callable, iterator, count, on_error, progress, cancel, unpack, use_async, **kwargs_extra)\u001b[0m\n\u001b[0;32m 108\u001b[0m \u001b[0miterator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_max_workers\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 109\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 110\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m 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"\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\concurrent\\futures\\_base.py\u001b[0m in \u001b[0;36mresult_iterator\u001b[1;34m()\u001b[0m\n\u001b[0;32m 617\u001b[0m \u001b[1;31m# Careful not to keep a reference to the popped future\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 618\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 619\u001b[1;33m \u001b[1;32myield\u001b[0m \u001b[0mfs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 620\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 621\u001b[0m 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KeyboardInterrupt)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 301\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 302\u001b[1;33m \u001b[0mwaiter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 303\u001b[0m \u001b[0mgotit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 304\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "for col_name in df.cols.names():\n", " print(col_name)\n", " %prun df[col_name].cols.quality(col_name)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'int' object has no attribute 'astype'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mRemoteTraceback\u001b[0m Traceback (most recent call last)", "\u001b[1;31mRemoteTraceback\u001b[0m: \n\"\"\"\nTraceback (most recent call last):\n File \"C:\\Users\\argenisleon\\Anaconda3\\envs\\python38\\lib\\multiprocessing\\pool.py\", line 125, in worker\n result = (True, func(*args, **kwds))\n File \"C:\\Users\\argenisleon\\Anaconda3\\envs\\python38\\lib\\site-packages\\vaex\\expression.py\", line 1696, in _apply\n scalar_result = self.f(*[fix_type(k[i]) for k in args], **{key: value[i] for key, value in kwargs.items()})\n File \"C:\\Users\\argenisleon\\Documents\\Optimus\\examples\\..\\optimus\\engines\\vaex\\functions.py\", line 94, in to_string_vaex\n return series.astype(str)\nAttributeError: 'int' object has no attribute 'astype'\n\"\"\"", "\nThe above exception was the direct cause of the following exception:\n", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mC:\\Users\\ARGENI~1\\AppData\\Local\\Temp/ipykernel_19528/3714096341.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount_uniques\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mcount_uniques\u001b[1;34m(self, cols, estimate, compute, tidy)\u001b[0m\n\u001b[0;32m 3170\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m \u001b[1;33m!=\u001b[0m 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for\n\u001b[0m\u001b[0;32m 1365\u001b[0m col_name in cols} for func in funcs}\n\u001b[0;32m 1366\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\functions.py\u001b[0m in \u001b[0;36mcount_uniques\u001b[1;34m(self, series, estimate)\u001b[0m\n\u001b[0;32m 450\u001b[0m \u001b[0mGet\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcount\u001b[0m \u001b[0mof\u001b[0m \u001b[0munique\u001b[0m \u001b[0mvalues\u001b[0m \u001b[1;32min\u001b[0m \u001b[0ma\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 451\u001b[0m \"\"\"\n\u001b[1;32m--> 452\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_string\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnunique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 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\u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 769\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 770\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 771\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 772\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 773\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 126\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 127\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mwrap_exception\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0m_helper_reraises_exception\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\vaex\\expression.py\u001b[0m in \u001b[0;36m_apply\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1694\u001b[0m \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mvaex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray_types\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1695\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1696\u001b[1;33m \u001b[0mscalar_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfix_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1697\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscalar_result\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1698\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\vaex\\functions.py\u001b[0m in \u001b[0;36mto_string_vaex\u001b[1;34m()\u001b[0m\n\u001b[0;32m 92\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mto_string\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 93\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mto_string_vaex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 94\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 95\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 96\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mseries\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_string_vaex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'int' object has no attribute 'astype'" ] } ], "source": [ "df.cols.count_uniques()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%prun df.cols.profile()" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "df.save.parquet(\"../../Data input/Base Pre2Pos para Modelo - Abr 22.parquet\")" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "dfv = df.data" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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MOVIL_POSPAGO
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Viewing 10 of 163.8 thousand rows / 1 columns
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1 partition(s) <class 'optimus.engines.pandas.dataframe.PandasDataFrame'>
\n", "\n" ], "text/plain": [ " MOVIL_POSPAGO\n", " (bool)\n", "---------------\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0\n", " 0" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mask.null(col_name)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'DataFrameLocal' object has no attribute 'count_uniques'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mC:\\Users\\ARGENI~1\\AppData\\Local\\Temp/ipykernel_35616/3714096341.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount_uniques\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mcount_uniques\u001b[1;34m(self, cols, estimate, compute, tidy)\u001b[0m\n\u001b[0;32m 3170\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mEngine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSPARK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mestimate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3171\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"'estimate' argument is only supported on {EnginePretty.SPARK.value}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3172\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg_exprs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount_uniques\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mestimate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtidy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3174\u001b[0m def _math(self, cols=\"*\", value=None, operator=None, output_cols=None, output_col=None, name=\"\",\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36magg_exprs\u001b[1;34m(self, cols, funcs, compute, tidy, parallel, *args)\u001b[0m\n\u001b[0;32m 1358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1359\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mparallel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1360\u001b[1;33m all_funcs = [getattr(df[cols].data, func.__name__)()\n\u001b[0m\u001b[0;32m 1361\u001b[0m for func in funcs]\n\u001b[0;32m 1362\u001b[0m \u001b[0magg_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_agg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_funcs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 1358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1359\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mparallel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1360\u001b[1;33m all_funcs = [getattr(df[cols].data, func.__name__)()\n\u001b[0m\u001b[0;32m 1361\u001b[0m for func in funcs]\n\u001b[0;32m 1362\u001b[0m \u001b[0magg_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_agg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_funcs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\vaex\\dataframe.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 282\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__hidden__\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__get__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 283\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 284\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 286\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_ipython_key_completions_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'DataFrameLocal' object has no attribute 'count_uniques'" ] } ], "source": [ "df.cols.sum()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] }, { "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute 'count_uniques'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mprofile\u001b[1;34m(self, cols, bins, flush)\u001b[0m\n\u001b[0;32m 827\u001b[0m \"\"\"\n\u001b[0;32m 828\u001b[0m \u001b[1;31m# Uses profile on self instead of calculate_profile to get the data only when it's necessary\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 829\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflush\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 830\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 831\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\profile.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, cols, bins, force_hist, output, flush, size)\u001b[0m\n\u001b[0;32m 84\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 85\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcalculate\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 86\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_calculate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforce_hist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflush\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 87\u001b[0m \u001b[0mprofile\u001b[0m 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148\u001b[1;33m \u001b[0mcols_data_types\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mcols_data_types\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols_to_infer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"infer_type\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 149\u001b[0m \u001b[0mcols_data_types\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mcols_data_types\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcols_to_profile\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcol\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcols_data_types\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36minfer_type\u001b[1;34m(self, cols, sample_count, tidy)\u001b[0m\n\u001b[0;32m 3662\u001b[0m \u001b[0msample_dtypes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_data_types\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfrequency\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3663\u001b[0m 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"\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36mcount_uniques\u001b[1;34m(self, cols, estimate, compute, tidy)\u001b[0m\n\u001b[0;32m 3170\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mEngine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSPARK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mestimate\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3171\u001b[0m \u001b[0mlogger\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"'estimate' argument is only supported on {EnginePretty.SPARK.value}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3172\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0magg_exprs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount_uniques\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mestimate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtidy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtidy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcompute\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3173\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3174\u001b[0m def _math(self, cols=\"*\", value=None, operator=None, output_cols=None, output_col=None, name=\"\",\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36magg_exprs\u001b[1;34m(self, cols, funcs, compute, tidy, parallel, *args)\u001b[0m\n\u001b[0;32m 1358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1359\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mparallel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1360\u001b[1;33m all_funcs = [getattr(df[cols].data, func.__name__)()\n\u001b[0m\u001b[0;32m 1361\u001b[0m for func in funcs]\n\u001b[0;32m 1362\u001b[0m \u001b[0magg_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_agg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_funcs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Documents\\Optimus\\examples\\..\\optimus\\engines\\base\\columns.py\u001b[0m in \u001b[0;36m\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 1358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1359\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mparallel\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1360\u001b[1;33m all_funcs = [getattr(df[cols].data, func.__name__)()\n\u001b[0m\u001b[0;32m 1361\u001b[0m for func in funcs]\n\u001b[0;32m 1362\u001b[0m \u001b[0magg_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexec_agg\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_funcs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompute\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m 5485\u001b[0m ):\n\u001b[0;32m 5486\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5487\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5488\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5489\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'count_uniques'" ] } ], "source": [ "%%time\n", "df.cols.profile(flush=True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NO\n", "missing 0.0\n", "Wall time: 86.9 ms\n", "CO_ID\n", "missing 0.0\n", "Wall time: 104 ms\n", "ARPU\n", "missing 0.0\n", "Wall time: 94.1 ms\n", "TENURE\n", "missing 0.0\n", "Wall time: 91.6 ms\n", "FECHA_INST_TELEFONO\n", "missing 0.0\n", "Wall time: 276 ms\n", "FECHA_INSTALACION\n", "missing 0.0\n", "Wall time: 262 ms\n", "STATUS_TELEFONO\n", "missing 0.0\n", "Wall time: 251 ms\n", "CANAL_VENTA\n", "missing 0.0\n", "Wall time: 113 ms\n", "SUB_CANAL_VENTA\n", "missing 0.0\n", "Wall time: 298 ms\n", "ID_VENDEDOR\n", "missing 0.0\n", "Wall time: 317 ms\n", "ID_PLAN\n", "missing 0.0\n", "Wall time: 265 ms\n", "PLAN_PREPAGO\n", "missing 0.0\n", "Wall time: 234 ms\n", "ID_PLAN_ANTERIOR\n", "missing 0.0\n", "Wall time: 260 ms\n", "FECHA_CAMBIO_PLAN\n", "missing 0.0\n", "Wall time: 251 ms\n", "TRA_IN_LOC_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 99.8 ms\n", "TRA_IN_INT_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 95.6 ms\n", "TRA_IN_NAT_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 111 ms\n", "TRA_IN_MOBILE_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 112 ms\n", "TRA_IN_MOBILE_OTHER_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 104 ms\n", "TRA_OUT_LOC_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 107 ms\n", "TRA_OUT_INT_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 126 ms\n", "TRA_OUT_NAT_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 113 ms\n", "TRA_OUT_MOBILE_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 109 ms\n", "TRA_OUT_MOBILE_OTHER_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 101 ms\n", "SMS_OUT_AMT_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 103 ms\n", "RECHARGE_AMT_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 115 ms\n", "AMT_RENT_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 105 ms\n", "AMT_MIN_OUT_INT_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 107 ms\n", "AMT_MIN_OUT_MOBILE_OTHER_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 117 ms\n", "AMT_MIN_OUT_OFF_NET_FIX_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 125 ms\n", "AMT_MIN_OUT_MOBILE_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 114 ms\n", "AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 110 ms\n", "AMT_ROAMING_SMS_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 104 ms\n", "AMT_ROAMING_DATA_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 107 ms\n", "AMT_ROAMING_VOICE_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 123 ms\n", "AMT_PAQUETICO_VOZ_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 116 ms\n", "AMT_PAQUETICO_DATA_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 131 ms\n", "AMT_MIN_OUT_ON_NET_FIX_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 131 ms\n", "MB_Mean_6_MESES\n", "missing 0.0\n", "Wall time: 127 ms\n", "TRA_IN_LOC_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 133 ms\n", "TRA_IN_INT_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 119 ms\n", "TRA_IN_NAT_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 171 ms\n", "TRA_IN_MOBILE_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 143 ms\n", "TRA_IN_MOBILE_OTHER_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 138 ms\n", "TRA_OUT_LOC_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 126 ms\n", "TRA_OUT_INT_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 129 ms\n", "TRA_OUT_NAT_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 132 ms\n", "TRA_OUT_MOBILE_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 136 ms\n", "TRA_OUT_MOBILE_OTHER_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 132 ms\n", "SMS_OUT_AMT_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 142 ms\n", "RECHARGE_AMT_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 111 ms\n", "AMT_RENT_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 114 ms\n", "AMT_MIN_OUT_INT_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 111 ms\n", "AMT_MIN_OUT_MOBILE_OTHER_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 107 ms\n", "AMT_MIN_OUT_OFF_NET_FIX_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 102 ms\n", "AMT_MIN_OUT_MOBILE_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 101 ms\n", "AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 107 ms\n", "AMT_ROAMING_SMS_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 99.9 ms\n", "AMT_ROAMING_DATA_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 116 ms\n", "AMT_ROAMING_VOICE_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 108 ms\n", "AMT_PAQUETICO_VOZ_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 100 ms\n", "AMT_PAQUETICO_DATA_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 107 ms\n", "AMT_MIN_OUT_ON_NET_FIX_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 111 ms\n", "MB_Mean_3_MESES\n", "missing 0.0\n", "Wall time: 119 ms\n", "FECHA_NACIMIENTO\n", "missing 0.0\n", "Wall time: 331 ms\n", "SEXO\n", "missing 0.0\n", "Wall time: 302 ms\n", "SEGMENTO_BI\n", "missing 0.0\n", "Wall time: 290 ms\n", "DESCRIPCION_PROVINCIA_CUENTA\n", "missing 0.0\n", "Wall time: 351 ms\n", "DESCRIPCION_PROVINCIA_CVOZ\n", "missing 0.0\n", "Wall time: 320 ms\n", "DESCRIPCION_PROVINCIA_CDATA\n", "missing 0.0\n", "Wall time: 268 ms\n", "PROVINCIA\n", "missing 0.0\n", "Wall time: 276 ms\n", "MONTO_RECARGA_Sum_MES_ANTERIOR\n", "missing 0.0\n", "Wall time: 257 ms\n", "CANT_RECARGAS_MES_ANTERIOR\n", "missing 0.0\n", "Wall time: 268 ms\n", "CABLE\n", "missing 0.0\n", "Wall time: 278 ms\n", "TELEFONO_FIJO\n", "missing 0.0\n", "Wall time: 282 ms\n", "INTERNET\n", "missing 0.0\n", "Wall time: 322 ms\n", "MBB\n", "missing 0.0\n", "Wall time: 344 ms\n", "MOVIL_POSPAGO\n", "missing 0.0\n", "Wall time: 308 ms\n" ] } ], "source": [ "for col_name in df.cols.names():\n", " print(col_name)\n", " %time df[col_name].cols.quality(col_name)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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None partition(s)
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NO
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CO_ID
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ARPU
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Viewing 10 of 163.8 thousand rows / 78 columns
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\n", "\n" ], "text/plain": [ "NO CO_ID ARPU TENURE FECHA_INST_TELEFONO FECHA_INSTALACION STATUS_TELEFONO CANAL_VENTA SUB_CANAL_VENTA ID_VENDEDOR ID_PLAN PLAN_PREPAGO ID_PLAN_ANTERIOR FECHA_CAMBIO_PLAN TRA_IN_LOC_Mean_6_MESES TRA_IN_INT_Mean_6_MESES TRA_IN_NAT_Mean_6_MESES TRA_IN_MOBILE_Mean_6_MESES TRA_IN_MOBILE_OTHER_Mean_6_MESES TRA_OUT_LOC_Mean_6_MESES TRA_OUT_INT_Mean_6_MESES TRA_OUT_NAT_Mean_6_MESES TRA_OUT_MOBILE_Mean_6_MESES TRA_OUT_MOBILE_OTHER_Mean_6_MESES SMS_OUT_AMT_Mean_6_MESES RECHARGE_AMT_Mean_6_MESES AMT_RENT_Mean_6_MESES AMT_MIN_OUT_INT_Mean_6_MESES AMT_MIN_OUT_MOBILE_OTHER_Mean_6_MESES AMT_MIN_OUT_OFF_NET_FIX_Mean_6_MESES AMT_MIN_OUT_MOBILE_Mean_6_MESES AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_6_MESES AMT_ROAMING_SMS_Mean_6_MESES AMT_ROAMING_DATA_Mean_6_MESES AMT_ROAMING_VOICE_Mean_6_MESES AMT_PAQUETICO_VOZ_Mean_6_MESES AMT_PAQUETICO_DATA_Mean_6_MESES AMT_MIN_OUT_ON_NET_FIX_Mean_6_MESES MB_Mean_6_MESES TRA_IN_LOC_Mean_3_MESES TRA_IN_INT_Mean_3_MESES TRA_IN_NAT_Mean_3_MESES TRA_IN_MOBILE_Mean_3_MESES TRA_IN_MOBILE_OTHER_Mean_3_MESES TRA_OUT_LOC_Mean_3_MESES TRA_OUT_INT_Mean_3_MESES TRA_OUT_NAT_Mean_3_MESES TRA_OUT_MOBILE_Mean_3_MESES TRA_OUT_MOBILE_OTHER_Mean_3_MESES SMS_OUT_AMT_Mean_3_MESES RECHARGE_AMT_Mean_3_MESES AMT_RENT_Mean_3_MESES AMT_MIN_OUT_INT_Mean_3_MESES AMT_MIN_OUT_MOBILE_OTHER_Mean_3_MESES AMT_MIN_OUT_OFF_NET_FIX_Mean_3_MESES AMT_MIN_OUT_MOBILE_Mean_3_MESES AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_3_MESES AMT_ROAMING_SMS_Mean_3_MESES AMT_ROAMING_DATA_Mean_3_MESES AMT_ROAMING_VOICE_Mean_3_MESES AMT_PAQUETICO_VOZ_Mean_3_MESES AMT_PAQUETICO_DATA_Mean_3_MESES AMT_MIN_OUT_ON_NET_FIX_Mean_3_MESES MB_Mean_3_MESES FECHA_NACIMIENTO SEXO SEGMENTO_BI DESCRIPCION_PROVINCIA_CUENTA DESCRIPCION_PROVINCIA_CVOZ DESCRIPCION_PROVINCIA_CDATA PROVINCIA MONTO_RECARGA_Sum_MES_ANTERIOR CANT_RECARGAS_MES_ANTERIOR CABLE TELEFONO_FIJO INTERNET MBB MOVIL_POSPAGO\n", "(bool) (int64) (int64) (int64) (string) (string) (string) (int64) (string) (string) (string) (string) (string) (string) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string)\n", "-------- --------- --------- --------- --------------------- ------------------- ----------------- ------------- ----------------- ------------- ---------- -------------- ------------------ ------------------- ------------------------- ------------------------- ------------------------- ---------------------------- ---------------------------------- -------------------------- -------------------------- -------------------------- ----------------------------- ----------------------------------- -------------------------- --------------------------- ----------------------- ------------------------------ --------------------------------------- -------------------------------------- --------------------------------- -------------------------------------------- ------------------------------ ------------------------------- -------------------------------- -------------------------------- --------------------------------- ------------------------------------- ----------------- ------------------------- ------------------------- ------------------------- ---------------------------- ---------------------------------- -------------------------- -------------------------- -------------------------- ----------------------------- ----------------------------------- -------------------------- --------------------------- ----------------------- ------------------------------ --------------------------------------- -------------------------------------- --------------------------------- -------------------------------------------- ------------------------------ ------------------------------- -------------------------------- -------------------------------- --------------------------------- ------------------------------------- ----------------- ------------------- ---------- ------------- ------------------------------ ---------------------------- ----------------------------- -------------------- -------------------------------- ---------------------------- ---------- --------------- ---------- ---------- ---------------\n", "False 12881892 384 109 2013-01-30 00:00:00 2013-01-30 00:00:00 A 6 4601 18425 CEPREVO005 S $null$ $null$ 1.55 0.986 2.384 17.903 35.277 4.271 0 2.16 6.134 7.396 3.143 340.651 0 0 0 0 0 0 0 0 0 0 0 0 0.014 1.763 1.32 3.703 17.527 39.467 2.58 0 0.76 8 5.837 5.333 282.047 0 0 0 0 0 0 0 0 0 0 0 0 0.011 1982-04-12 00:00:00 M E SAN PEDRO DE MACORIS SAN PEDRO DE MACORIS $null$ SAN PEDRO DE MACORIS 76.920 1 1 $null$ $null$ $null$ $null$\n", "False 136449998 410 32 2019-07-19 00:00:00 2022-02-06 00:00:00 A 3 2903 24425 CEPREVO004 S $null$ $null$ 3.323 0 0 126.01 5.379 0.007 0 0 18.44 1.474 4 393.626 0 0 0 0 0 0 0 0 0 0 0 0 3505.54 2.173 0 0 128.703 4.597 0.017 0 0 0.753 0.1 5.333 197.433 0 0 0 0 0 0 0 0 0 0 0 0 2940.91 1995-05-10 00:00:00 F $null$ DUARTE DUARTE $null$ DUARTE $null$ $null$ $null$ $null$ $null$ 1 $null$\n", "False 15661796 654 93 2014-06-20 00:00:00 2014-06-20 00:00:00 A 6 358 19394 CEPREVO005 S $null$ $null$ 0.124 0 0 110.039 87.179 0.549 0 0 9.119 5.939 16.143 515.26 0 0 0 0 0 0 0 0 0 0 0 0 20121.6 0.29 0 0 112.61 19.15 0 0 0 12.01 9.71 14.333 575.98 0 0 0 0 0 0 0 0 0 0 0 0 23524.5 1993-02-15 00:00:00 F E DISTRITO NACIONAL SAN JUAN $null$ SAN JUAN 804.880 8 $null$ $null$ $null$ $null$ $null$\n", "False 127068927 350 37 2019-01-29 00:00:00 2019-01-31 00:00:00 A 3 1919 23502 CEPREVO005 S $null$ $null$ 0.566 0 0 3.311 3.483 0 0 0 0.069 0.471 0 212.297 0 0 0 0 0 0 0 0 0 0 0 0 1296.56 0 0 0 0.493 5.147 0 0 0 0.16 1.1 0 38.46 0 0 0 0 0 0 0 0 0 0 0 0 1.238 1973-09-30 00:00:00 F E AZUA SANTIAGO $null$ SANTIAGO 115.380 2 $null$ $null$ $null$ $null$ $null$\n", "False 156732494 660 9 2021-06-14 00:00:00 2021-06-14 00:00:00 A 6 11361 E6314 CEPREVO005 S $null$ $null$ 0.049 0 0 1.137 10.38 0.263 0 0 1.071 4.966 2 348.34 0 0 0 0 0 0 0 0 0 0 0 0 11663.5 0 0 0 0.653 0.433 0.613 0 0 1.12 2.133 2.667 307.68 0 0 0 0 0 0 0 0 0 0 0 0 11349.3 1997-07-30 00:00:00 F E DISTRITO NACIONAL SANTO DOMINGO $null$ SANTO DOMINGO $null$ $null$ $null$ $null$ $null$ $null$ $null$\n", "False 158282264 741 7 2021-07-30 00:00:00 2021-07-30 00:00:00 A 3 5598 25430 CEPREVO005 S $null$ $null$ 0 0 0 0.3 3.246 0 0 0 1.094 5.943 1.857 63.954 0 0 0 0 0 0 0 0 0 0 0 0 164.832 0 0 0 0 0.713 0 0 0 0 0 2.333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1990-12-24 00:00:00 M E BAHORUCO $null$ $null$ BAHORUCO $null$ $null$ $null$ $null$ $null$ $null$ $null$\n", "False 73882646 596 59 2017-04-20 00:00:00 2020-07-07 00:00:00 A 3 1938 S4801 CEPREVO004 S $null$ $null$ 0.976 0 0 15.146 36.881 0 0 0 0.946 1.706 0 495.603 0 0 0 0 0 0 0 0 0 0 0 0 10044.9 2.277 0 0 27.12 36.703 0 0 0 0.72 0 0 215.38 0 0 0 0 0 0 0 0 0 0 0 0 2453.69 1976-09-09 00:00:00 M E DISTRITO NACIONAL SANTO DOMINGO $null$ SANTO DOMINGO $null$ $null$ $null$ $null$ $null$ $null$ $null$\n", "False 3385917 288 181 2007-02-19 00:00:00 2015-12-20 00:00:00 A 6 66 999 CEPREVO006 S $null$ $null$ 0.481 0 0 1.901 3.589 0.583 1.4 0 0 1.174 0 159.337 0 0 0 0 0 0 0 0 0 0 0 0 385.892 1.123 0 0 4.437 8.373 1.36 3.267 0 0 2.74 0 371.787 0 0 0 0 0 0 0 0 0 0 0 0 900.414 1972-04-19 00:00:00 F E DISTRITO NACIONAL DISTRITO NACIONAL $null$ DISTRITO NACIONAL 346.140 4 $null$ $null$ $null$ $null$ $null$\n", "False 116622990 713 44 2018-06-29 00:00:00 2018-06-29 00:00:00 A 6 10700 D8917 CEPREVO005 S CEFLEVO006 2020-10-26 15:41:53 4.917 4.233 0.094 8.399 12.047 0.637 0.003 0.243 5.5 1.93 2.143 691.541 0 0 0 0 0 0 0 0 0 0 0 0 16461.5 5.42 9.877 0.22 9.877 8.323 1.267 0.007 0 4.613 2.01 3 736.663 0 0 0 0 0 0 0 0 0 0 0 0 20158.1 1952-04-30 00:00:00 F D PUERTO PLATA SANTIAGO $null$ SANTIAGO 645.390 5 $null$ $null$ $null$ 1 $null$\n", "False 81969494 702 57 2017-06-20 00:00:00 2019-08-14 00:00:00 A 6 215 D3359 CEPREVO005 S $null$ $null$ 5.589 35.849 0 8.55 22.851 1.337 0 0 28.023 12.806 5.714 679.137 0 0 0 0 0 0 0 0 0 0 0 0 0.03 3.253 51.24 0 3.423 8.573 0 0 0 11.827 2.413 6.667 320.5 0 0 0 0 0 0 0 0 0 0 0 0 0.003 1988-11-24 00:00:00 F E MARIA TRINIDAD SANCHEZ SAMANA SAMANA SAMANA 192.300 4 $null$ $null$ $null$ $null$ $null$\n", "False 132727116 341 34 2019-05-14 00:00:00 2019-05-14 00:00:00 A 7 2799 F3816 CEPREVO005 S $null$ $null$ 1.84 0 0 1.764 17.446 4.134 0 0 20.21 27.907 0 362.646 0 0 0 0 0 0 0 0 0 0 0 0 0 2.113 0 0 2.67 14.993 0 0 0 31.097 22.003 0 325.647 0 0 0 0 0 0 0 0 0 0 0 0 0 1956-11-04 00:00:00 F E SAN CRISTOBAL SANTIAGO $null$ SANTIAGO 392.300 10 $null$ $null$ $null$ $null$ $null$" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.mask.null(\"NO\")" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "df.data[\"CO_ID\"] =\"1\"" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "dfv = df.data[\"CO_ID\"]\n", "dfv\n", "def func(value):\n", " return value\n", "\n", "dfv = df.data.apply(func, dfv)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'int' object is not subscriptable", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\IPython\\core\\formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, obj, include, exclude)\u001b[0m\n\u001b[0;32m 968\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 969\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 970\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 971\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 972\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\vaex\\dataframe.py\u001b[0m in \u001b[0;36m_repr_mimebundle_\u001b[1;34m(self, include, exclude, **kwargs)\u001b[0m\n\u001b[0;32m 4207\u001b[0m \u001b[1;31m# TODO: optimize, since we use the same data in both versions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4208\u001b[0m \u001b[1;31m# TODO: include latex version\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4209\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m'text/html'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_head_and_tail_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'html'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'text/plain'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_head_and_tail_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'plain'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4210\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4211\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_repr_html_\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\vaex\\dataframe.py\u001b[0m in \u001b[0;36m_head_and_tail_table\u001b[1;34m(self, n, format)\u001b[0m\n\u001b[0;32m 3962\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_as_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3963\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3964\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_as_table\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mceil\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mN\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3966\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mhead_and_tail_print\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\vaex\\dataframe.py\u001b[0m in \u001b[0;36m_as_table\u001b[1;34m(self, i1, i2, j1, j2, format, ellipsis)\u001b[0m\n\u001b[0;32m 4116\u001b[0m \u001b[1;31m# return values_list\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4117\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mi2\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mi1\u001b[0m \u001b[1;33m>\u001b[0m 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\u001b[0mvalues_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mellipsis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m~\\Anaconda3\\envs\\python38\\lib\\site-packages\\vaex\\dataframe.py\u001b[0m in \u001b[0;36mtable_part\u001b[1;34m(k1, k2, parts)\u001b[0m\n\u001b[0;32m 4110\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcolumns_sliced\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mj\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mcolumns_sliced\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4111\u001b[0m \u001b[0mcolumn_index\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;31m# skip over the 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obj)\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_method\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 344\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmethod\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 345\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 346\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 347\u001b[0m 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\u001b[1;32mif\u001b[0m \u001b[0mcolumns_sliced\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mj\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mcolumns_sliced\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4111\u001b[0m \u001b[0mcolumn_index\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;31m# skip over the slice/ellipsis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4112\u001b[1;33m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4113\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_format_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4114\u001b[0m \u001b[0mvalues_list\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumn_index\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: 'int' object is not subscriptable" ] } ], "source": [ "df.data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9a84a194ddf849b4920b8de7d29665d8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import vaex\n", "with vaex.progress.tree('rich', title=\"Two passes\"):\n", " df.cols.sum(\"*\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "limit 10\n" ] }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
Viewing 10 of 163.8 thousand rows / 78 columns
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None partition(s)
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NO
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1 (int64)
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CO_ID
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2 (int64)
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ARPU
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3 (int64)
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TENURE
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4 (int64)
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FECHA_INST_TELEFONO
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5 (string)
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FECHA_INSTALACION
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Viewing 10 of 163.8 thousand rows / 78 columns
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None partition(s) <class 'optimus.engines.vaex.dataframe.VaexDataFrame'>
\n", "\n" ], "text/plain": [ " NO CO_ID ARPU TENURE FECHA_INST_TELEFONO FECHA_INSTALACION STATUS_TELEFONO CANAL_VENTA SUB_CANAL_VENTA ID_VENDEDOR ID_PLAN PLAN_PREPAGO ID_PLAN_ANTERIOR FECHA_CAMBIO_PLAN TRA_IN_LOC_Mean_6_MESES TRA_IN_INT_Mean_6_MESES TRA_IN_NAT_Mean_6_MESES TRA_IN_MOBILE_Mean_6_MESES TRA_IN_MOBILE_OTHER_Mean_6_MESES TRA_OUT_LOC_Mean_6_MESES TRA_OUT_INT_Mean_6_MESES TRA_OUT_NAT_Mean_6_MESES TRA_OUT_MOBILE_Mean_6_MESES TRA_OUT_MOBILE_OTHER_Mean_6_MESES SMS_OUT_AMT_Mean_6_MESES RECHARGE_AMT_Mean_6_MESES AMT_RENT_Mean_6_MESES AMT_MIN_OUT_INT_Mean_6_MESES AMT_MIN_OUT_MOBILE_OTHER_Mean_6_MESES AMT_MIN_OUT_OFF_NET_FIX_Mean_6_MESES AMT_MIN_OUT_MOBILE_Mean_6_MESES AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_6_MESES AMT_ROAMING_SMS_Mean_6_MESES AMT_ROAMING_DATA_Mean_6_MESES AMT_ROAMING_VOICE_Mean_6_MESES AMT_PAQUETICO_VOZ_Mean_6_MESES AMT_PAQUETICO_DATA_Mean_6_MESES AMT_MIN_OUT_ON_NET_FIX_Mean_6_MESES MB_Mean_6_MESES TRA_IN_LOC_Mean_3_MESES TRA_IN_INT_Mean_3_MESES TRA_IN_NAT_Mean_3_MESES TRA_IN_MOBILE_Mean_3_MESES TRA_IN_MOBILE_OTHER_Mean_3_MESES TRA_OUT_LOC_Mean_3_MESES TRA_OUT_INT_Mean_3_MESES TRA_OUT_NAT_Mean_3_MESES TRA_OUT_MOBILE_Mean_3_MESES TRA_OUT_MOBILE_OTHER_Mean_3_MESES SMS_OUT_AMT_Mean_3_MESES RECHARGE_AMT_Mean_3_MESES AMT_RENT_Mean_3_MESES AMT_MIN_OUT_INT_Mean_3_MESES AMT_MIN_OUT_MOBILE_OTHER_Mean_3_MESES AMT_MIN_OUT_OFF_NET_FIX_Mean_3_MESES AMT_MIN_OUT_MOBILE_Mean_3_MESES AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_3_MESES AMT_ROAMING_SMS_Mean_3_MESES AMT_ROAMING_DATA_Mean_3_MESES AMT_ROAMING_VOICE_Mean_3_MESES AMT_PAQUETICO_VOZ_Mean_3_MESES AMT_PAQUETICO_DATA_Mean_3_MESES AMT_MIN_OUT_ON_NET_FIX_Mean_3_MESES MB_Mean_3_MESES FECHA_NACIMIENTO SEXO SEGMENTO_BI DESCRIPCION_PROVINCIA_CUENTA DESCRIPCION_PROVINCIA_CVOZ DESCRIPCION_PROVINCIA_CDATA PROVINCIA MONTO_RECARGA_Sum_MES_ANTERIOR CANT_RECARGAS_MES_ANTERIOR CABLE TELEFONO_FIJO INTERNET MBB MOVIL_POSPAGO\n", " (int64) (int64) (int64) (int64) (string) (string) (string) (int64) (string) (string) (string) (string) 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---------------------------------- -------------------------- -------------------------- -------------------------- ----------------------------- ----------------------------------- -------------------------- --------------------------- ----------------------- ------------------------------ --------------------------------------- -------------------------------------- --------------------------------- -------------------------------------------- ------------------------------ ------------------------------- -------------------------------- -------------------------------- --------------------------------- ------------------------------------- ----------------- ------------------------- ------------------------- ------------------------- ---------------------------- ---------------------------------- -------------------------- -------------------------- -------------------------- ----------------------------- ----------------------------------- -------------------------- --------------------------- ----------------------- ------------------------------ --------------------------------------- -------------------------------------- --------------------------------- -------------------------------------------- ------------------------------ ------------------------------- -------------------------------- -------------------------------- --------------------------------- ------------------------------------- ----------------- ------------------- ---------- ------------- ------------------------------ ---------------------------- ----------------------------- -------------------- -------------------------------- ---------------------------- ---------- --------------- ---------- ---------- ---------------\n", " 1 12881892 384 109 2013-01-30 00:00:00 2013-01-30 00:00:00 A 6 4601 18425 CEPREVO005 S $null$ $null$ 1.55 0.986 2.384 17.903 35.277 4.271 0 2.16 6.134 7.396 3.143 340.651 0 0 0 0 0 0 0 0 0 0 0 0 0.014 1.763 1.32 3.703 17.527 39.467 2.58 0 0.76 8 5.837 5.333 282.047 0 0 0 0 0 0 0 0 0 0 0 0 0.011 1982-04-12 00:00:00 M E SAN PEDRO DE MACORIS SAN PEDRO DE MACORIS $null$ SAN PEDRO DE MACORIS 76.920 1 1 $null$ $null$ $null$ $null$\n", " 2 136449998 410 32 2019-07-19 00:00:00 2022-02-06 00:00:00 A 3 2903 24425 CEPREVO004 S $null$ $null$ 3.323 0 0 126.01 5.379 0.007 0 0 18.44 1.474 4 393.626 0 0 0 0 0 0 0 0 0 0 0 0 3505.54 2.173 0 0 128.703 4.597 0.017 0 0 0.753 0.1 5.333 197.433 0 0 0 0 0 0 0 0 0 0 0 0 2940.91 1995-05-10 00:00:00 F $null$ DUARTE DUARTE $null$ DUARTE $null$ $null$ $null$ $null$ $null$ 1 $null$\n", " 3 15661796 654 93 2014-06-20 00:00:00 2014-06-20 00:00:00 A 6 358 19394 CEPREVO005 S $null$ $null$ 0.124 0 0 110.039 87.179 0.549 0 0 9.119 5.939 16.143 515.26 0 0 0 0 0 0 0 0 0 0 0 0 20121.6 0.29 0 0 112.61 19.15 0 0 0 12.01 9.71 14.333 575.98 0 0 0 0 0 0 0 0 0 0 0 0 23524.5 1993-02-15 00:00:00 F E DISTRITO NACIONAL SAN JUAN $null$ SAN JUAN 804.880 8 $null$ $null$ $null$ $null$ $null$\n", " 4 127068927 350 37 2019-01-29 00:00:00 2019-01-31 00:00:00 A 3 1919 23502 CEPREVO005 S $null$ $null$ 0.566 0 0 3.311 3.483 0 0 0 0.069 0.471 0 212.297 0 0 0 0 0 0 0 0 0 0 0 0 1296.56 0 0 0 0.493 5.147 0 0 0 0.16 1.1 0 38.46 0 0 0 0 0 0 0 0 0 0 0 0 1.238 1973-09-30 00:00:00 F E AZUA SANTIAGO $null$ SANTIAGO 115.380 2 $null$ $null$ $null$ $null$ $null$\n", " 5 156732494 660 9 2021-06-14 00:00:00 2021-06-14 00:00:00 A 6 11361 E6314 CEPREVO005 S $null$ $null$ 0.049 0 0 1.137 10.38 0.263 0 0 1.071 4.966 2 348.34 0 0 0 0 0 0 0 0 0 0 0 0 11663.5 0 0 0 0.653 0.433 0.613 0 0 1.12 2.133 2.667 307.68 0 0 0 0 0 0 0 0 0 0 0 0 11349.3 1997-07-30 00:00:00 F E DISTRITO NACIONAL SANTO DOMINGO $null$ SANTO DOMINGO $null$ $null$ $null$ $null$ $null$ $null$ $null$\n", " 6 158282264 741 7 2021-07-30 00:00:00 2021-07-30 00:00:00 A 3 5598 25430 CEPREVO005 S $null$ $null$ 0 0 0 0.3 3.246 0 0 0 1.094 5.943 1.857 63.954 0 0 0 0 0 0 0 0 0 0 0 0 164.832 0 0 0 0 0.713 0 0 0 0 0 2.333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1990-12-24 00:00:00 M E BAHORUCO $null$ $null$ BAHORUCO $null$ $null$ $null$ $null$ $null$ $null$ $null$\n", " 7 73882646 596 59 2017-04-20 00:00:00 2020-07-07 00:00:00 A 3 1938 S4801 CEPREVO004 S $null$ $null$ 0.976 0 0 15.146 36.881 0 0 0 0.946 1.706 0 495.603 0 0 0 0 0 0 0 0 0 0 0 0 10044.9 2.277 0 0 27.12 36.703 0 0 0 0.72 0 0 215.38 0 0 0 0 0 0 0 0 0 0 0 0 2453.69 1976-09-09 00:00:00 M E DISTRITO NACIONAL SANTO DOMINGO $null$ SANTO DOMINGO $null$ $null$ $null$ $null$ $null$ $null$ $null$\n", " 8 3385917 288 181 2007-02-19 00:00:00 2015-12-20 00:00:00 A 6 66 999 CEPREVO006 S $null$ $null$ 0.481 0 0 1.901 3.589 0.583 1.4 0 0 1.174 0 159.337 0 0 0 0 0 0 0 0 0 0 0 0 385.892 1.123 0 0 4.437 8.373 1.36 3.267 0 0 2.74 0 371.787 0 0 0 0 0 0 0 0 0 0 0 0 900.414 1972-04-19 00:00:00 F E DISTRITO NACIONAL DISTRITO NACIONAL $null$ DISTRITO NACIONAL 346.140 4 $null$ $null$ $null$ $null$ $null$\n", " 9 116622990 713 44 2018-06-29 00:00:00 2018-06-29 00:00:00 A 6 10700 D8917 CEPREVO005 S CEFLEVO006 2020-10-26 15:41:53 4.917 4.233 0.094 8.399 12.047 0.637 0.003 0.243 5.5 1.93 2.143 691.541 0 0 0 0 0 0 0 0 0 0 0 0 16461.5 5.42 9.877 0.22 9.877 8.323 1.267 0.007 0 4.613 2.01 3 736.663 0 0 0 0 0 0 0 0 0 0 0 0 20158.1 1952-04-30 00:00:00 F D PUERTO PLATA SANTIAGO $null$ SANTIAGO 645.390 5 $null$ $null$ $null$ 1 $null$\n", " 10 81969494 702 57 2017-06-20 00:00:00 2019-08-14 00:00:00 A 6 215 D3359 CEPREVO005 S $null$ $null$ 5.589 35.849 0 8.55 22.851 1.337 0 0 28.023 12.806 5.714 679.137 0 0 0 0 0 0 0 0 0 0 0 0 0.03 3.253 51.24 0 3.423 8.573 0 0 0 11.827 2.413 6.667 320.5 0 0 0 0 0 0 0 0 0 0 0 0 0.003 1988-11-24 00:00:00 F E MARIA TRINIDAD SANCHEZ SAMANA SAMANA SAMANA 192.300 4 $null$ $null$ $null$ $null$ $null$\n", " 11 132727116 341 34 2019-05-14 00:00:00 2019-05-14 00:00:00 A 7 2799 F3816 CEPREVO005 S $null$ $null$ 1.84 0 0 1.764 17.446 4.134 0 0 20.21 27.907 0 362.646 0 0 0 0 0 0 0 0 0 0 0 0 0 2.113 0 0 2.67 14.993 0 0 0 31.097 22.003 0 325.647 0 0 0 0 0 0 0 0 0 0 0 0 0 1956-11-04 00:00:00 F E SAN CRISTOBAL 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Viewing 10 of 163.8 thousand rows / 78 columns
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None partition(s)
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Viewing 10 of 163.8 thousand rows / 78 columns
\n", "
None partition(s) <class 'optimus.engines.vaex.dataframe.VaexDataFrame'>
\n", "\n" ], "text/plain": [ " NO CO_ID ARPU TENURE FECHA_INST_TELEFONO FECHA_INSTALACION STATUS_TELEFONO CANAL_VENTA SUB_CANAL_VENTA ID_VENDEDOR ID_PLAN PLAN_PREPAGO ID_PLAN_ANTERIOR FECHA_CAMBIO_PLAN TRA_IN_LOC_Mean_6_MESES TRA_IN_INT_Mean_6_MESES TRA_IN_NAT_Mean_6_MESES TRA_IN_MOBILE_Mean_6_MESES TRA_IN_MOBILE_OTHER_Mean_6_MESES TRA_OUT_LOC_Mean_6_MESES TRA_OUT_INT_Mean_6_MESES TRA_OUT_NAT_Mean_6_MESES TRA_OUT_MOBILE_Mean_6_MESES TRA_OUT_MOBILE_OTHER_Mean_6_MESES SMS_OUT_AMT_Mean_6_MESES RECHARGE_AMT_Mean_6_MESES AMT_RENT_Mean_6_MESES AMT_MIN_OUT_INT_Mean_6_MESES AMT_MIN_OUT_MOBILE_OTHER_Mean_6_MESES AMT_MIN_OUT_OFF_NET_FIX_Mean_6_MESES AMT_MIN_OUT_MOBILE_Mean_6_MESES AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_6_MESES AMT_ROAMING_SMS_Mean_6_MESES AMT_ROAMING_DATA_Mean_6_MESES AMT_ROAMING_VOICE_Mean_6_MESES AMT_PAQUETICO_VOZ_Mean_6_MESES AMT_PAQUETICO_DATA_Mean_6_MESES AMT_MIN_OUT_ON_NET_FIX_Mean_6_MESES MB_Mean_6_MESES TRA_IN_LOC_Mean_3_MESES TRA_IN_INT_Mean_3_MESES TRA_IN_NAT_Mean_3_MESES TRA_IN_MOBILE_Mean_3_MESES TRA_IN_MOBILE_OTHER_Mean_3_MESES TRA_OUT_LOC_Mean_3_MESES TRA_OUT_INT_Mean_3_MESES TRA_OUT_NAT_Mean_3_MESES TRA_OUT_MOBILE_Mean_3_MESES TRA_OUT_MOBILE_OTHER_Mean_3_MESES SMS_OUT_AMT_Mean_3_MESES RECHARGE_AMT_Mean_3_MESES AMT_RENT_Mean_3_MESES AMT_MIN_OUT_INT_Mean_3_MESES AMT_MIN_OUT_MOBILE_OTHER_Mean_3_MESES AMT_MIN_OUT_OFF_NET_FIX_Mean_3_MESES AMT_MIN_OUT_MOBILE_Mean_3_MESES AMT_MIN_OUT_MOBILE_SPE_TARIFF_Mean_3_MESES AMT_ROAMING_SMS_Mean_3_MESES AMT_ROAMING_DATA_Mean_3_MESES AMT_ROAMING_VOICE_Mean_3_MESES AMT_PAQUETICO_VOZ_Mean_3_MESES AMT_PAQUETICO_DATA_Mean_3_MESES AMT_MIN_OUT_ON_NET_FIX_Mean_3_MESES MB_Mean_3_MESES FECHA_NACIMIENTO SEXO SEGMENTO_BI DESCRIPCION_PROVINCIA_CUENTA DESCRIPCION_PROVINCIA_CVOZ DESCRIPCION_PROVINCIA_CDATA PROVINCIA MONTO_RECARGA_Sum_MES_ANTERIOR CANT_RECARGAS_MES_ANTERIOR CABLE TELEFONO_FIJO INTERNET MBB MOVIL_POSPAGO\n", " (int64) (int64) (int64) (int64) (string) (string) (string) (int64) (string) (string) (string) (string) (string) (string) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (float64) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string) (string)\n", "--------- --------- --------- --------- --------------------- ------------------- ----------------- ------------- ----------------- ------------- ---------- -------------- ------------------ ------------------- ------------------------- ------------------------- ------------------------- ---------------------------- ---------------------------------- -------------------------- -------------------------- -------------------------- ----------------------------- ----------------------------------- -------------------------- --------------------------- ----------------------- ------------------------------ --------------------------------------- -------------------------------------- --------------------------------- -------------------------------------------- ------------------------------ ------------------------------- -------------------------------- -------------------------------- --------------------------------- ------------------------------------- ----------------- ------------------------- ------------------------- ------------------------- ---------------------------- ---------------------------------- -------------------------- -------------------------- -------------------------- ----------------------------- ----------------------------------- -------------------------- --------------------------- ----------------------- ------------------------------ --------------------------------------- -------------------------------------- --------------------------------- -------------------------------------------- ------------------------------ ------------------------------- -------------------------------- -------------------------------- --------------------------------- ------------------------------------- ----------------- ------------------- ---------- ------------- ------------------------------ ---------------------------- ----------------------------- -------------------- -------------------------------- ---------------------------- ---------- --------------- ---------- ---------- ---------------\n", " 1 12881892 384 109 2013-01-30 00:00:00 2013-01-30 00:00:00 A 6 4601 18425 CEPREVO005 S $null$ $null$ 1.55 0.986 2.384 17.903 35.277 4.271 0 2.16 6.134 7.396 3.143 340.651 0 0 0 0 0 0 0 0 0 0 0 0 0.014 1.763 1.32 3.703 17.527 39.467 2.58 0 0.76 8 5.837 5.333 282.047 0 0 0 0 0 0 0 0 0 0 0 0 0.011 1982-04-12 00:00:00 M E SAN PEDRO DE MACORIS SAN PEDRO DE MACORIS $null$ SAN PEDRO DE MACORIS 76.920 1 1 $null$ $null$ $null$ $null$\n", " 2 136449998 410 32 2019-07-19 00:00:00 2022-02-06 00:00:00 A 3 2903 24425 CEPREVO004 S $null$ $null$ 3.323 0 0 126.01 5.379 0.007 0 0 18.44 1.474 4 393.626 0 0 0 0 0 0 0 0 0 0 0 0 3505.54 2.173 0 0 128.703 4.597 0.017 0 0 0.753 0.1 5.333 197.433 0 0 0 0 0 0 0 0 0 0 0 0 2940.91 1995-05-10 00:00:00 F $null$ DUARTE DUARTE $null$ DUARTE $null$ $null$ $null$ $null$ $null$ 1 $null$\n", " 3 15661796 654 93 2014-06-20 00:00:00 2014-06-20 00:00:00 A 6 358 19394 CEPREVO005 S $null$ $null$ 0.124 0 0 110.039 87.179 0.549 0 0 9.119 5.939 16.143 515.26 0 0 0 0 0 0 0 0 0 0 0 0 20121.6 0.29 0 0 112.61 19.15 0 0 0 12.01 9.71 14.333 575.98 0 0 0 0 0 0 0 0 0 0 0 0 23524.5 1993-02-15 00:00:00 F E DISTRITO NACIONAL SAN JUAN $null$ SAN JUAN 804.880 8 $null$ $null$ $null$ $null$ $null$\n", " 4 127068927 350 37 2019-01-29 00:00:00 2019-01-31 00:00:00 A 3 1919 23502 CEPREVO005 S $null$ $null$ 0.566 0 0 3.311 3.483 0 0 0 0.069 0.471 0 212.297 0 0 0 0 0 0 0 0 0 0 0 0 1296.56 0 0 0 0.493 5.147 0 0 0 0.16 1.1 0 38.46 0 0 0 0 0 0 0 0 0 0 0 0 1.238 1973-09-30 00:00:00 F E AZUA SANTIAGO $null$ SANTIAGO 115.380 2 $null$ $null$ $null$ $null$ $null$\n", " 5 156732494 660 9 2021-06-14 00:00:00 2021-06-14 00:00:00 A 6 11361 E6314 CEPREVO005 S $null$ $null$ 0.049 0 0 1.137 10.38 0.263 0 0 1.071 4.966 2 348.34 0 0 0 0 0 0 0 0 0 0 0 0 11663.5 0 0 0 0.653 0.433 0.613 0 0 1.12 2.133 2.667 307.68 0 0 0 0 0 0 0 0 0 0 0 0 11349.3 1997-07-30 00:00:00 F E DISTRITO NACIONAL SANTO DOMINGO $null$ SANTO DOMINGO $null$ $null$ $null$ $null$ $null$ $null$ $null$\n", " 6 158282264 741 7 2021-07-30 00:00:00 2021-07-30 00:00:00 A 3 5598 25430 CEPREVO005 S $null$ $null$ 0 0 0 0.3 3.246 0 0 0 1.094 5.943 1.857 63.954 0 0 0 0 0 0 0 0 0 0 0 0 164.832 0 0 0 0 0.713 0 0 0 0 0 2.333 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1990-12-24 00:00:00 M E BAHORUCO $null$ $null$ BAHORUCO $null$ $null$ $null$ $null$ $null$ $null$ $null$\n", " 7 73882646 596 59 2017-04-20 00:00:00 2020-07-07 00:00:00 A 3 1938 S4801 CEPREVO004 S $null$ $null$ 0.976 0 0 15.146 36.881 0 0 0 0.946 1.706 0 495.603 0 0 0 0 0 0 0 0 0 0 0 0 10044.9 2.277 0 0 27.12 36.703 0 0 0 0.72 0 0 215.38 0 0 0 0 0 0 0 0 0 0 0 0 2453.69 1976-09-09 00:00:00 M E DISTRITO NACIONAL SANTO DOMINGO $null$ SANTO DOMINGO $null$ $null$ $null$ $null$ $null$ $null$ $null$\n", " 8 3385917 288 181 2007-02-19 00:00:00 2015-12-20 00:00:00 A 6 66 999 CEPREVO006 S $null$ $null$ 0.481 0 0 1.901 3.589 0.583 1.4 0 0 1.174 0 159.337 0 0 0 0 0 0 0 0 0 0 0 0 385.892 1.123 0 0 4.437 8.373 1.36 3.267 0 0 2.74 0 371.787 0 0 0 0 0 0 0 0 0 0 0 0 900.414 1972-04-19 00:00:00 F E DISTRITO NACIONAL DISTRITO NACIONAL $null$ DISTRITO NACIONAL 346.140 4 $null$ $null$ $null$ $null$ $null$\n", " 9 116622990 713 44 2018-06-29 00:00:00 2018-06-29 00:00:00 A 6 10700 D8917 CEPREVO005 S CEFLEVO006 2020-10-26 15:41:53 4.917 4.233 0.094 8.399 12.047 0.637 0.003 0.243 5.5 1.93 2.143 691.541 0 0 0 0 0 0 0 0 0 0 0 0 16461.5 5.42 9.877 0.22 9.877 8.323 1.267 0.007 0 4.613 2.01 3 736.663 0 0 0 0 0 0 0 0 0 0 0 0 20158.1 1952-04-30 00:00:00 F D PUERTO PLATA SANTIAGO $null$ SANTIAGO 645.390 5 $null$ $null$ $null$ 1 $null$\n", " 10 81969494 702 57 2017-06-20 00:00:00 2019-08-14 00:00:00 A 6 215 D3359 CEPREVO005 S $null$ $null$ 5.589 35.849 0 8.55 22.851 1.337 0 0 28.023 12.806 5.714 679.137 0 0 0 0 0 0 0 0 0 0 0 0 0.03 3.253 51.24 0 3.423 8.573 0 0 0 11.827 2.413 6.667 320.5 0 0 0 0 0 0 0 0 0 0 0 0 0.003 1988-11-24 00:00:00 F E MARIA TRINIDAD SANCHEZ SAMANA SAMANA SAMANA 192.300 4 $null$ $null$ $null$ $null$ $null$\n", " 11 132727116 341 34 2019-05-14 00:00:00 2019-05-14 00:00:00 A 7 2799 F3816 CEPREVO005 S $null$ $null$ 1.84 0 0 1.764 17.446 4.134 0 0 20.21 27.907 0 362.646 0 0 0 0 0 0 0 0 0 0 0 0 0 2.113 0 0 2.67 14.993 0 0 0 31.097 22.003 0 325.647 0 0 0 0 0 0 0 0 0 0 0 0 0 1956-11-04 00:00:00 F E SAN CRISTOBAL SANTIAGO $null$ SANTIAGO 392.300 10 $null$ $null$ $null$ $null$ $null$" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "df" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "21 ms ± 2.06 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], "source": [ "%timeit df.data.sum(df.cols.names([0,1,2,3]))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dccd05ae17c74aa9b829cc7632c07353", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 603 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import vaex\n",
    "with vaex.progress.tree('rich', title=\"Two passes\"):\n",
    "    dff = df.cols.to_float()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfv = dff.data"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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