diff --git a/notebooks/libraries/Numpy and Pandas.ipynb b/notebooks/libraries/Numpy and Pandas.ipynb new file mode 100644 index 0000000..6c2207a --- /dev/null +++ b/notebooks/libraries/Numpy and Pandas.ipynb @@ -0,0 +1,8118 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "ImportError", + "evalue": "No module named numpy", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mImportError\u001b[0m: No module named numpy" + ] + } + ], + "source": [ + "import numpy as np" + ] + }, + { + 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"execute_result" + } + ], + "source": [ + "b[2:3,1:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "a=np.arange(15)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [ 5, 6, 7, 8, 9],\n", + " [10, 11, 12, 13, 14]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + 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"b=b.reshape(3,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2, 3, 4, 5, 6],\n", + " [ 7, 8, 9, 10, 11],\n", + " [12, 13, 14, 15, 16]])" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "c=a+b" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3, 4],\n", + " [ 5, 6, 7, 8, 9],\n", + " [10, 11, 12, 13, 14]])" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "c=a*b" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 5)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2, 3, 4],\n", + " [ 5, 6, 7],\n", + " [ 8, 9, 10],\n", + " [11, 12, 13],\n", + " [14, 15, 16]])" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b.reshape(5,3)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[150, 170, 190],\n", + " [300, 345, 390],\n", + " [450, 520, 590]])" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b.dot(a.reshape(5,3))" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100]])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d=np.full((12,5),100,dtype=int)\n", + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100],\n", + " [100, 100, 100, 100, 100]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# PANDAS START HERE" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df=pd.read_csv(\"HR.CSV\",sep=\",\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + 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NameDepartmentGEORoleRising_StarWill_RelocateCriticalTrending PerfTalent_LevelValidated_Talent_Level...Emp_Competitive_4Emp_Competitive_5Emp_Collaborative_1Emp_Collaborative_2Emp_Collaborative_3Emp_Collaborative_4Emp_Collaborative_5Sensor_StepCountSensor_Heartbeat(Average/Min)Sensor_Proximity(1-highest/10-lowest)
2BIRDWELLFinanceFranceSenior DirectorNaN0NaN366...25224443193808
3BENTHuman ResourcesChinaSenior DirectorNaN0NaN366...51124413248776
4BAZANITKoreaDirectorNaN0NaN366...535125431498010
5BANKWarehouseColombiaDirectorNaN0NaN366...232242412048510
6BANASOperationsAustraliaDirectorNaN1NaN366...325154518017010
7BADESalesTurkeyDirectorNaN1NaN366...12114111940866
8AUBREYFinanceJapanDirectorNaN0NaN366...51523321974647
9ARANGOHuman ResourcesUKDirectorNaN1NaN366...34423341185638
10AHEARNITChinaSenior ManagerNaN01.0366...452145421218010
11ADDISWarehouseKoreaSenior ManagerNaN11.0366...15125243554789
12ZUMWALTOperationsUKSenior ManagerNaN01.0366...45214523056769
13WURTHSalesColombiaSenior ManagerNaN01.0366...131213334577510
14WILKFinanceKoreaSenior ManagerNaN01.0366...211222527747610
15WIDENERHuman ResourcesFranceSenior ManagerNaN01.0366...15123521262636
16WAGSTAFFITUSSenior ManagerNaN11.0366...35524121544659
17VELLAWarehouseChinaSenior ManagerNaN01.0366...41411442908908
18URRUTIAOperationsUSSenior ManagerNaN11.0366...33424252081906
19TERWILLIGERSalesAustraliaSenior ManagerNaN01.0366...111122211806810
20TARTFinanceAustraliaSenior ManagerNaN11.0366...21213313951887
21STEINMANHuman ResourcesKoreaSenior ManagerNaN11.0366...14413132656858
22STAATSITChinaManagerNaN1NaN366...52215121922737
23SLOATWarehouseKoreaManagerNaN0NaN366...45414423644757
24RIVESOperationsAustraliaManagerNaN1NaN366...12213441713737
25RIGGLESalesFranceManagerNaN1NaN366...55312232925868
26REVELSFinanceFranceManagerNaN1NaN366...15324412773896
27REICHARDHuman ResourcesJapanManagerNaN1NaN366...31315413375808
28PRICKETTITAustraliaManagerNaN1NaN366...31322251470879
29POFFWarehouseUSManagerNaN0NaN366...133243311719010
30PITZEROperationsUKManagerNaN1NaN366...541223215618810
31PETROSalesColombiaManagerNaN0NaN366...341254410977310
..................................................................
14969AMBROSEWarehouseChinaManagerNaN0NaN366...53112351524863
14970ZAPATAOperationsJapanManagerNaN1NaN366...51111451272865
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14974CALLITUKManagerNaN1NaN366...11412131505894
14975DOVEWarehouseJapanManagerNaN1NaN366...34555311276865
14976BOUDREAUXOperationsJapanManagerNaN1NaN366...23222211631851
14977ARAGONSalesKoreaLevel 2-4NaN0NaN366...34421341561872
14978WHITLOCKFinanceChinaLevel 2-4NaN1NaN366...53511521362891
14979TREJOHuman ResourcesUKLevel 2-4NaN1NaN366...34123421086856
14980TACKETTITChinaLevel 2-4NaN0NaN366...51412351649877
14981SHEARERWarehouseAustraliaLevel 2-4NaN1NaN366...23154551196861
14982SALDANAOperationsUSLevel 2-4NaN1NaN366...31124451623909
14983HANKSSalesKoreaLevel 2-4NaN0NaN366...35123441235898
14984GOLDFinanceKoreaLevel 2-4NaN0NaN366...41523141102877
14985DRIVERHuman ResourcesKoreaLevel 2-4NaN1NaN366...45121321040907
14986MCKINNONITUSLevel 2-4NaN1NaN366...55122451458876
14987KOEHLERWarehouseFranceLevel 2-4NaN1NaN366...33511211846858
14988CHAMPAGNEOperationsFranceLevel 2-4NaN0NaN366...44515251830871
14989BOURGEOISSalesTurkeyLevel 2-4NaN0NaN366...24524121603894
14990POOLFinanceTurkeyLevel 2-4NaN0NaN366...41315441098881
14991KEYESHuman ResourcesFranceLevel 2-4NaN1NaN366...23214341227867
14992GOODSONITChinaLevel 2-4NaN0NaN366...22413431906885
14993FOOTEWarehouseUKLevel 2-4NaN1NaN366...11525141150902
14994EARLYOperationsKoreaLevel 2-4NaN1NaN366...32422311538889
14995LUNSFORDSalesAustraliaLevel 2-4NaN1NaN366...44314551247894
14996GOLDSMITHFinanceChinaLevel 2-4NaN0NaN366...24423451155882
14997FLOODHuman ResourcesUSLevel 2-4NaN0NaN366...15213511210865
14998YoloITColombiaLevel 2-4NaN1NaN366...15215311639864
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14997 rows × 61 columns

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" + ], + "text/plain": [ + " Name Department GEO Role Rising_Star \\\n", + "2 BIRDWELL Finance France Senior Director NaN \n", + "3 BENT Human Resources China Senior Director NaN \n", + "4 BAZAN IT Korea Director NaN \n", + "5 BANK Warehouse Colombia Director NaN \n", + "6 BANAS Operations Australia Director NaN \n", + "7 BADE Sales Turkey Director NaN \n", + "8 AUBREY Finance Japan Director NaN \n", + "9 ARANGO Human Resources UK Director NaN \n", + "10 AHEARN IT China Senior Manager NaN \n", + "11 ADDIS Warehouse Korea Senior Manager NaN \n", + "12 ZUMWALT Operations UK Senior Manager NaN \n", + "13 WURTH Sales Colombia Senior Manager NaN \n", + "14 WILK Finance Korea Senior Manager NaN \n", + "15 WIDENER Human Resources France Senior Manager NaN \n", + "16 WAGSTAFF IT US Senior Manager NaN \n", + "17 VELLA Warehouse China Senior Manager NaN \n", + "18 URRUTIA Operations US Senior Manager NaN \n", + "19 TERWILLIGER Sales Australia Senior Manager NaN \n", + "20 TART Finance Australia Senior Manager NaN \n", + "21 STEINMAN Human Resources Korea Senior Manager NaN \n", + "22 STAATS IT China Manager NaN \n", + "23 SLOAT Warehouse Korea Manager NaN \n", + "24 RIVES Operations Australia Manager NaN \n", + "25 RIGGLE Sales France Manager NaN \n", + "26 REVELS Finance France Manager NaN \n", + "27 REICHARD Human Resources Japan Manager NaN \n", + "28 PRICKETT IT Australia Manager NaN \n", + "29 POFF Warehouse US Manager NaN \n", + "30 PITZER Operations UK Manager NaN \n", + "31 PETRO Sales Colombia Manager NaN \n", + "... ... ... ... ... ... \n", + "14969 AMBROSE Warehouse China Manager NaN \n", + "14970 ZAPATA Operations Japan Manager NaN \n", + "14971 HYATT Sales China Manager NaN \n", + "14972 HEMPHILL Finance Turkey Manager NaN \n", + "14973 FAULK Human Resources US Manager NaN \n", + "14974 CALL IT UK Manager NaN \n", + "14975 DOVE Warehouse Japan Manager NaN \n", + "14976 BOUDREAUX Operations Japan Manager NaN \n", + "14977 ARAGON Sales Korea Level 2-4 NaN \n", + "14978 WHITLOCK Finance China Level 2-4 NaN \n", + "14979 TREJO Human Resources UK Level 2-4 NaN \n", + "14980 TACKETT IT China Level 2-4 NaN \n", + "14981 SHEARER Warehouse Australia Level 2-4 NaN \n", + "14982 SALDANA Operations US Level 2-4 NaN \n", + "14983 HANKS Sales Korea Level 2-4 NaN \n", + "14984 GOLD Finance Korea Level 2-4 NaN \n", + "14985 DRIVER Human Resources Korea Level 2-4 NaN \n", + "14986 MCKINNON IT US Level 2-4 NaN \n", + "14987 KOEHLER Warehouse France Level 2-4 NaN \n", + "14988 CHAMPAGNE Operations France Level 2-4 NaN \n", + "14989 BOURGEOIS Sales Turkey Level 2-4 NaN \n", + "14990 POOL Finance Turkey Level 2-4 NaN \n", + "14991 KEYES Human Resources France Level 2-4 NaN \n", + "14992 GOODSON IT China Level 2-4 NaN \n", + "14993 FOOTE Warehouse UK Level 2-4 NaN \n", + "14994 EARLY Operations Korea Level 2-4 NaN \n", + "14995 LUNSFORD Sales Australia Level 2-4 NaN \n", + "14996 GOLDSMITH Finance China Level 2-4 NaN \n", + "14997 FLOOD Human Resources US Level 2-4 NaN \n", + "14998 Yolo IT Colombia Level 2-4 NaN \n", + "\n", + " Will_Relocate Critical Trending Perf Talent_Level \\\n", + "2 0 NaN 3 6 \n", + "3 0 NaN 3 6 \n", + "4 0 NaN 3 6 \n", + "5 0 NaN 3 6 \n", + "6 1 NaN 3 6 \n", + "7 1 NaN 3 6 \n", + "8 0 NaN 3 6 \n", + "9 1 NaN 3 6 \n", + "10 0 1.0 3 6 \n", + "11 1 1.0 3 6 \n", + "12 0 1.0 3 6 \n", + "13 0 1.0 3 6 \n", + "14 0 1.0 3 6 \n", + "15 0 1.0 3 6 \n", + "16 1 1.0 3 6 \n", + "17 0 1.0 3 6 \n", + "18 1 1.0 3 6 \n", + "19 0 1.0 3 6 \n", + "20 1 1.0 3 6 \n", + "21 1 1.0 3 6 \n", + "22 1 NaN 3 6 \n", + "23 0 NaN 3 6 \n", + "24 1 NaN 3 6 \n", + "25 1 NaN 3 6 \n", + "26 1 NaN 3 6 \n", + "27 1 NaN 3 6 \n", + "28 1 NaN 3 6 \n", + "29 0 NaN 3 6 \n", + "30 1 NaN 3 6 \n", + "31 0 NaN 3 6 \n", + "... ... ... ... ... \n", + "14969 0 NaN 3 6 \n", + "14970 1 NaN 3 6 \n", + "14971 1 NaN 3 6 \n", + "14972 1 NaN 3 6 \n", + "14973 1 NaN 3 6 \n", + "14974 1 NaN 3 6 \n", + "14975 1 NaN 3 6 \n", + "14976 1 NaN 3 6 \n", + "14977 0 NaN 3 6 \n", + "14978 1 NaN 3 6 \n", + "14979 1 NaN 3 6 \n", + "14980 0 NaN 3 6 \n", + "14981 1 NaN 3 6 \n", + "14982 1 NaN 3 6 \n", + "14983 0 NaN 3 6 \n", + "14984 0 NaN 3 6 \n", + "14985 1 NaN 3 6 \n", + "14986 1 NaN 3 6 \n", + "14987 1 NaN 3 6 \n", + "14988 0 NaN 3 6 \n", + "14989 0 NaN 3 6 \n", + "14990 0 NaN 3 6 \n", + "14991 1 NaN 3 6 \n", + "14992 0 NaN 3 6 \n", + "14993 1 NaN 3 6 \n", + "14994 1 NaN 3 6 \n", + "14995 1 NaN 3 6 \n", + "14996 0 NaN 3 6 \n", + "14997 0 NaN 3 6 \n", + "14998 1 NaN 3 6 \n", + "\n", + " Validated_Talent_Level ... \\\n", + "2 6 ... \n", + "3 6 ... \n", + "4 6 ... \n", + "5 6 ... \n", + "6 6 ... \n", + "7 6 ... \n", + "8 6 ... \n", + "9 6 ... \n", + "10 6 ... \n", + "11 6 ... \n", + "12 6 ... \n", + "13 6 ... \n", + "14 6 ... \n", + "15 6 ... \n", + "16 6 ... \n", + "17 6 ... \n", + "18 6 ... \n", + "19 6 ... \n", + "20 6 ... \n", + "21 6 ... \n", + "22 6 ... \n", + "23 6 ... \n", + "24 6 ... \n", + "25 6 ... \n", + "26 6 ... \n", + "27 6 ... \n", + "28 6 ... \n", + "29 6 ... \n", + "30 6 ... \n", + "31 6 ... \n", + "... ... ... \n", + "14969 6 ... \n", + "14970 6 ... \n", + "14971 6 ... \n", + "14972 6 ... \n", + "14973 6 ... \n", + "14974 6 ... \n", + "14975 6 ... \n", + "14976 6 ... \n", + "14977 6 ... \n", + "14978 6 ... \n", + "14979 6 ... \n", + "14980 6 ... \n", + "14981 6 ... \n", + "14982 6 ... \n", + "14983 6 ... \n", + "14984 6 ... \n", + "14985 6 ... \n", + "14986 6 ... \n", + "14987 6 ... \n", + "14988 6 ... \n", + "14989 6 ... \n", + "14990 6 ... \n", + "14991 6 ... \n", + "14992 6 ... \n", + "14993 6 ... \n", + "14994 6 ... \n", + "14995 6 ... \n", + "14996 6 ... \n", + "14997 6 ... \n", + "14998 6 ... \n", + "\n", + " Emp_Competitive_4 Emp_Competitive_5 Emp_Collaborative_1 \\\n", + "2 2 5 2 \n", + "3 5 1 1 \n", + "4 5 3 5 \n", + "5 2 3 2 \n", + "6 3 2 5 \n", + "7 1 2 1 \n", + "8 5 1 5 \n", + "9 3 4 4 \n", + "10 4 5 2 \n", + "11 1 5 1 \n", + "12 4 5 2 \n", + "13 1 3 1 \n", + "14 2 1 1 \n", + "15 1 5 1 \n", + "16 3 5 5 \n", + "17 4 1 4 \n", + "18 3 3 4 \n", + "19 1 1 1 \n", + "20 2 1 2 \n", + "21 1 4 4 \n", + "22 5 2 2 \n", + "23 4 5 4 \n", + "24 1 2 2 \n", + "25 5 5 3 \n", + "26 1 5 3 \n", + "27 3 1 3 \n", + "28 3 1 3 \n", + "29 1 3 3 \n", + "30 5 4 1 \n", + "31 3 4 1 \n", + "... ... ... ... \n", + "14969 5 3 1 \n", + "14970 5 1 1 \n", + "14971 2 2 3 \n", + "14972 4 1 3 \n", + "14973 1 3 5 \n", + "14974 1 1 4 \n", + "14975 3 4 5 \n", + "14976 2 3 2 \n", + "14977 3 4 4 \n", + "14978 5 3 5 \n", + "14979 3 4 1 \n", + "14980 5 1 4 \n", + "14981 2 3 1 \n", + "14982 3 1 1 \n", + "14983 3 5 1 \n", + "14984 4 1 5 \n", + "14985 4 5 1 \n", + "14986 5 5 1 \n", + "14987 3 3 5 \n", + "14988 4 4 5 \n", + "14989 2 4 5 \n", + "14990 4 1 3 \n", + "14991 2 3 2 \n", + "14992 2 2 4 \n", + "14993 1 1 5 \n", + "14994 3 2 4 \n", + "14995 4 4 3 \n", + "14996 2 4 4 \n", + "14997 1 5 2 \n", + "14998 1 5 2 \n", + "\n", + " Emp_Collaborative_2 Emp_Collaborative_3 Emp_Collaborative_4 \\\n", + "2 2 4 4 \n", + "3 2 4 4 \n", + "4 1 2 5 \n", + "5 2 4 2 \n", + "6 1 5 4 \n", + "7 1 4 1 \n", + "8 2 3 3 \n", + "9 2 3 3 \n", + "10 1 4 5 \n", + "11 2 5 2 \n", + "12 1 4 5 \n", + "13 2 1 3 \n", + "14 2 2 2 \n", + "15 2 3 5 \n", + "16 2 4 1 \n", + "17 1 1 4 \n", + "18 2 4 2 \n", + "19 1 2 2 \n", + "20 1 3 3 \n", + "21 1 3 1 \n", + "22 1 5 1 \n", + "23 1 4 4 \n", + "24 1 3 4 \n", + "25 1 2 2 \n", + "26 2 4 4 \n", + "27 1 5 4 \n", + "28 2 2 2 \n", + "29 2 4 3 \n", + "30 2 2 3 \n", + "31 2 5 4 \n", + "... ... ... ... \n", + "14969 1 2 3 \n", + "14970 1 1 4 \n", + "14971 1 5 3 \n", + "14972 2 3 5 \n", + "14973 1 2 5 \n", + "14974 1 2 1 \n", + "14975 5 5 3 \n", + "14976 2 2 2 \n", + "14977 2 1 3 \n", + "14978 1 1 5 \n", + "14979 2 3 4 \n", + "14980 1 2 3 \n", + "14981 5 4 5 \n", + "14982 2 4 4 \n", + "14983 2 3 4 \n", + "14984 2 3 1 \n", + "14985 2 1 3 \n", + "14986 2 2 4 \n", + "14987 1 1 2 \n", + "14988 1 5 2 \n", + "14989 2 4 1 \n", + "14990 1 5 4 \n", + "14991 1 4 3 \n", + "14992 1 3 4 \n", + "14993 2 5 1 \n", + "14994 2 2 3 \n", + "14995 1 4 5 \n", + "14996 2 3 4 \n", + "14997 1 3 5 \n", + "14998 1 5 3 \n", + "\n", + " Emp_Collaborative_5 Sensor_StepCount Sensor_Heartbeat(Average/Min) \\\n", + "2 4 3193 80 \n", + "3 1 3248 77 \n", + "4 4 3149 80 \n", + "5 4 1204 85 \n", + "6 5 1801 70 \n", + "7 1 1940 86 \n", + "8 2 1974 64 \n", + "9 4 1185 63 \n", + "10 4 2121 80 \n", + "11 4 3554 78 \n", + "12 2 3056 76 \n", + "13 3 3457 75 \n", + "14 5 2774 76 \n", + "15 2 1262 63 \n", + "16 2 1544 65 \n", + "17 4 2908 90 \n", + "18 5 2081 90 \n", + "19 2 1180 68 \n", + "20 1 3951 88 \n", + "21 3 2656 85 \n", + "22 2 1922 73 \n", + "23 2 3644 75 \n", + "24 4 1713 73 \n", + "25 3 2925 86 \n", + "26 1 2773 89 \n", + "27 1 3375 80 \n", + "28 5 1470 87 \n", + "29 3 1171 90 \n", + "30 2 1561 88 \n", + "31 4 1097 73 \n", + "... ... ... ... \n", + "14969 5 1524 86 \n", + "14970 5 1272 86 \n", + "14971 3 1718 87 \n", + "14972 3 1762 89 \n", + "14973 2 1923 89 \n", + "14974 3 1505 89 \n", + "14975 1 1276 86 \n", + "14976 1 1631 85 \n", + "14977 4 1561 87 \n", + "14978 2 1362 89 \n", + "14979 2 1086 85 \n", + "14980 5 1649 87 \n", + "14981 5 1196 86 \n", + "14982 5 1623 90 \n", + "14983 4 1235 89 \n", + "14984 4 1102 87 \n", + "14985 2 1040 90 \n", + "14986 5 1458 87 \n", + "14987 1 1846 85 \n", + "14988 5 1830 87 \n", + "14989 2 1603 89 \n", + "14990 4 1098 88 \n", + "14991 4 1227 86 \n", + "14992 3 1906 88 \n", + "14993 4 1150 90 \n", + "14994 1 1538 88 \n", + "14995 5 1247 89 \n", + "14996 5 1155 88 \n", + "14997 1 1210 86 \n", + "14998 1 1639 86 \n", + "\n", + " Sensor_Proximity(1-highest/10-lowest) \n", + "2 8 \n", + "3 6 \n", + "4 10 \n", + "5 10 \n", + "6 10 \n", + "7 6 \n", + "8 7 \n", + "9 8 \n", + "10 10 \n", + "11 9 \n", + "12 9 \n", + "13 10 \n", + "14 10 \n", + "15 6 \n", + "16 9 \n", + "17 8 \n", + "18 6 \n", + "19 10 \n", + "20 7 \n", + "21 8 \n", + "22 7 \n", + "23 7 \n", + "24 7 \n", + "25 8 \n", + "26 6 \n", + "27 8 \n", + "28 9 \n", + "29 10 \n", + "30 10 \n", + "31 10 \n", + "... ... \n", + "14969 3 \n", + "14970 5 \n", + "14971 1 \n", + "14972 8 \n", + "14973 1 \n", + "14974 4 \n", + "14975 5 \n", + "14976 1 \n", + "14977 2 \n", + "14978 1 \n", + "14979 6 \n", + "14980 7 \n", + "14981 1 \n", + "14982 9 \n", + "14983 8 \n", + "14984 7 \n", + "14985 7 \n", + "14986 6 \n", + "14987 8 \n", + "14988 1 \n", + "14989 4 \n", + "14990 1 \n", + "14991 7 \n", + "14992 5 \n", + "14993 2 \n", + "14994 9 \n", + "14995 4 \n", + "14996 2 \n", + "14997 5 \n", + "14998 4 \n", + "\n", + "[14997 rows x 61 columns]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_2" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\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----> 1\u001b[1;33m \u001b[0mdf2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Department\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m\"IT\"\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"GEO\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m\"China\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 953\u001b[0m raise ValueError(\"The truth value of a {0} is ambiguous. \"\n\u001b[0;32m 954\u001b[0m \u001b[1;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 955\u001b[1;33m .format(self.__class__.__name__))\n\u001b[0m\u001b[0;32m 956\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 957\u001b[0m \u001b[0m__bool__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()." + ] + } + ], + "source": [ + "df2=df[(df[\"Department\"]==\"IT\" and df[\"GEO\"]==\"China\")]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2500 rows × 62 columns

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Emp_Competitive_4 \\\n", + "4 ... 5 \n", + "10 ... 4 \n", + "16 ... 3 \n", + "22 ... 5 \n", + "28 ... 3 \n", + "34 ... 2 \n", + "40 ... 3 \n", + "46 ... 1 \n", + "52 ... 4 \n", + "58 ... 4 \n", + "64 ... 3 \n", + "70 ... 5 \n", + "76 ... 1 \n", + "82 ... 4 \n", + "88 ... 3 \n", + "94 ... 2 \n", + "100 ... 1 \n", + "106 ... 4 \n", + "112 ... 3 \n", + "118 ... 4 \n", + "124 ... 4 \n", + "130 ... 3 \n", + "136 ... 5 \n", + "142 ... 4 \n", + "148 ... 3 \n", + "154 ... 5 \n", + "160 ... 5 \n", + "166 ... 2 \n", + "172 ... 2 \n", + "178 ... 2 \n", + "... ... ... \n", + "14824 ... 3 \n", + "14830 ... 3 \n", + "14836 ... 3 \n", + "14842 ... 4 \n", + "14848 ... 2 \n", + "14854 ... 3 \n", + "14860 ... 4 \n", + "14866 ... 3 \n", + "14872 ... 3 \n", + "14878 ... 3 \n", + "14884 ... 1 \n", + "14890 ... 5 \n", + "14896 ... 4 \n", + "14902 ... 4 \n", + "14908 ... 5 \n", + "14914 ... 3 \n", + "14920 ... 2 \n", + "14926 ... 3 \n", + "14932 ... 2 \n", + "14938 ... 1 \n", + "14944 ... 3 \n", + "14950 ... 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IDNameDepartmentGEORoleRising_StarWill_RelocateCriticalTrending PerfTalent_Level...Emp_Competitive_4Emp_Competitive_5Emp_Collaborative_1Emp_Collaborative_2Emp_Collaborative_3Emp_Collaborative_4Emp_Collaborative_5Sensor_StepCountSensor_Heartbeat(Average/Min)Sensor_Proximity(1-highest/10-lowest)
01BRADDYOperationsUSVPNaN01.036...22222151841619
12BORSTSalesUKSenior DirectorNaN0NaN36...41325151990908
23BIRDWELLFinanceFranceSenior DirectorNaN0NaN36...25224443193808
34BENTHuman ResourcesChinaSenior DirectorNaN0NaN36...51124413248776
45BAZANITKoreaDirectorNaN0NaN36...535125431498010
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IDNameDepartmentGEORoleRising_StarWill_RelocateCriticalTrending PerfTalent_Level...Emp_Competitive_4Emp_Competitive_5Emp_Collaborative_1Emp_Collaborative_2Emp_Collaborative_3Emp_Collaborative_4Emp_Collaborative_5Sensor_StepCountSensor_Heartbeat(Average/Min)Sensor_Proximity(1-highest/10-lowest)
01BRADDYOperationsUSVPNaN01.036...22222151841619
12BORSTSalesUKSenior DirectorNaN0NaN36...41325151990908
23BIRDWELLFinanceFranceSenior DirectorNaN0NaN36...25224443193808
34BENTHuman ResourcesChinaSenior DirectorNaN0NaN36...51124413248776
45BAZANITKoreaDirectorNaN0NaN36...535125431498010
56BANKWarehouseColombiaDirectorNaN0NaN36...232242412048510
67BANASOperationsAustraliaDirectorNaN1NaN36...325154518017010
78BADESalesTurkeyDirectorNaN1NaN36...12114111940866
89AUBREYFinanceJapanDirectorNaN0NaN36...51523321974647
910ARANGOHuman ResourcesUKDirectorNaN1NaN36...34423341185638
1011AHEARNITChinaSenior ManagerNaN01.036...452145421218010
1112ADDISWarehouseKoreaSenior ManagerNaN11.036...15125243554789
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12 rows × 62 columns

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CountryNameCountryCodeIndicatorNameIndicatorCodeYearValue
0Arab WorldARBAdolescent fertility rate (births per 1,000 wo...SP.ADO.TFRT19601.335609e+02
1Arab WorldARBAge dependency ratio (% of working-age populat...SP.POP.DPND19608.779760e+01
2Arab WorldARBAge dependency ratio, old (% of working-age po...SP.POP.DPND.OL19606.634579e+00
3Arab WorldARBAge dependency ratio, young (% of working-age ...SP.POP.DPND.YG19608.102333e+01
4Arab WorldARBArms exports (SIPRI trend indicator values)MS.MIL.XPRT.KD19603.000000e+06
5Arab WorldARBArms imports (SIPRI trend indicator values)MS.MIL.MPRT.KD19605.380000e+08
6Arab WorldARBBirth rate, crude (per 1,000 people)SP.DYN.CBRT.IN19604.769789e+01
7Arab WorldARBCO2 emissions (kt)EN.ATM.CO2E.KT19605.956399e+04
8Arab WorldARBCO2 emissions (metric tons per capita)EN.ATM.CO2E.PC19606.439635e-01
9Arab WorldARBCO2 emissions from gaseous fuel consumption (%...EN.ATM.CO2E.GF.ZS19605.041292e+00
10Arab WorldARBCO2 emissions from liquid fuel consumption (% ...EN.ATM.CO2E.LF.ZS19608.485147e+01
11Arab WorldARBCO2 emissions from liquid fuel consumption (kt)EN.ATM.CO2E.LF.KT19604.954171e+04
12Arab WorldARBCO2 emissions from solid fuel consumption (% o...EN.ATM.CO2E.SF.ZS19604.726981e+00
13Arab WorldARBDeath rate, crude (per 1,000 people)SP.DYN.CDRT.IN19601.975445e+01
14Arab WorldARBFertility rate, total (births per woman)SP.DYN.TFRT.IN19606.924027e+00
15Arab WorldARBFixed telephone subscriptionsIT.MLT.MAIN19604.068330e+05
16Arab WorldARBFixed telephone subscriptions (per 100 people)IT.MLT.MAIN.P219606.167006e-01
17Arab WorldARBHospital beds (per 1,000 people)SH.MED.BEDS.ZS19601.929622e+00
18Arab WorldARBInternational migrant stock (% of population)SM.POP.TOTL.ZS19602.990637e+00
19Arab WorldARBInternational migrant stock, totalSM.POP.TOTL19603.324685e+06
20Arab WorldARBLife expectancy at birth, female (years)SP.DYN.LE00.FE.IN19604.788325e+01
21Arab WorldARBLife expectancy at birth, male (years)SP.DYN.LE00.MA.IN19604.586295e+01
22Arab WorldARBLife expectancy at birth, total (years)SP.DYN.LE00.IN19604.684706e+01
23Arab WorldARBMerchandise exports (current US$)TX.VAL.MRCH.CD.WT19604.645919e+09
24Arab WorldARBMerchandise exports by the reporting economy (...TX.VAL.MRCH.WL.CD19602.468800e+09
25Arab WorldARBMerchandise exports by the reporting economy, ...TX.VAL.MRCH.RS.ZS19601.646954e+01
26Arab WorldARBMerchandise exports to developing economies in...TX.VAL.MRCH.R1.ZS19602.260207e+00
27Arab WorldARBMerchandise exports to developing economies in...TX.VAL.MRCH.R3.ZS19604.496111e-01
28Arab WorldARBMerchandise exports to developing economies in...TX.VAL.MRCH.R4.ZS19606.379618e+00
29Arab WorldARBMerchandise exports to developing economies in...TX.VAL.MRCH.R5.ZS19602.790830e+00
.....................
5656428ZimbabweZWEPPG, multilateral (INT, current US$)DT.INT.MLAT.CD20151.626000e+06
5656429ZimbabweZWEPPG, official creditors (AMT, current US$)DT.AMT.OFFT.CD20151.663930e+08
5656430ZimbabweZWEPPG, official creditors (DIS, current US$)DT.DIS.OFFT.CD20158.377700e+07
5656431ZimbabweZWEPPG, official creditors (INT, current US$)DT.INT.OFFT.CD20152.321300e+07
5656432ZimbabweZWEPPG, other private creditors (AMT, current US$)DT.AMT.PROP.CD20156.250000e+05
5656433ZimbabweZWEPPG, other private creditors (DIS, current US$)DT.DIS.PROP.CD20150.000000e+00
5656434ZimbabweZWEPPG, other private creditors (INT, current US$)DT.INT.PROP.CD20151.000000e+03
5656435ZimbabweZWEPPG, private creditors (AMT, current US$)DT.AMT.PRVT.CD20156.250000e+05
5656436ZimbabweZWEPPG, private creditors (DIS, current US$)DT.DIS.PRVT.CD20150.000000e+00
5656437ZimbabweZWEPPG, private creditors (INT, current US$)DT.INT.PRVT.CD20151.000000e+03
5656438ZimbabweZWEPrincipal repayments on external debt, long-te...DT.AMT.DLXF.CD20151.670180e+08
5656439ZimbabweZWEPrincipal repayments on external debt, private...DT.AMT.DPNG.CD20150.000000e+00
5656440ZimbabweZWEPrivate credit bureau coverage (% of adults)IC.CRD.PRVT.ZS20153.210000e+01
5656441ZimbabweZWEProcedures to build a warehouse (number)IC.WRH.PROC20151.000000e+01
5656442ZimbabweZWEProcedures to register property (number)IC.PRP.PROC20155.000000e+00
5656443ZimbabweZWEProfit tax (% of commercial profits)IC.TAX.PRFT.CP.ZS20151.880000e+01
5656444ZimbabweZWEProportion of seats held by women in national ...SG.GEN.PARL.ZS20153.148148e+01
5656445ZimbabweZWEPublic credit registry coverage (% of adults)IC.CRD.PUBL.ZS20150.000000e+00
5656446ZimbabweZWESource data assessment of statistical capacity...IQ.SCI.SRCE20156.000000e+01
5656447ZimbabweZWEStart-up procedures to register a business (nu...IC.REG.PROC20159.000000e+00
5656448ZimbabweZWEStrength of legal rights index (0=weak to 12=s...IC.LGL.CRED.XQ20155.000000e+00
5656449ZimbabweZWETax payments (number)IC.TAX.PAYM20154.900000e+01
5656450ZimbabweZWETime required to build a warehouse (days)IC.WRH.DURS20154.480000e+02
5656451ZimbabweZWETime required to enforce a contract (days)IC.LGL.DURS20154.100000e+02
5656452ZimbabweZWETime required to get electricity (days)IC.ELC.TIME20151.060000e+02
5656453ZimbabweZWETime required to register property (days)IC.PRP.DURS20153.600000e+01
5656454ZimbabweZWETime required to start a business (days)IC.REG.DURS20159.000000e+01
5656455ZimbabweZWETime to prepare and pay taxes (hours)IC.TAX.DURS20152.420000e+02
5656456ZimbabweZWETime to resolve insolvency (years)IC.ISV.DURS20153.300000e+00
5656457ZimbabweZWETotal tax rate (% of commercial profits)IC.TAX.TOTL.CP.ZS20153.280000e+01
\n", + "

5656458 rows × 6 columns

\n", + "
" + ], + "text/plain": [ + " CountryName CountryCode \\\n", + "0 Arab World ARB \n", + "1 Arab World ARB \n", + "2 Arab World ARB \n", + "3 Arab World ARB \n", + "4 Arab World ARB \n", + "5 Arab World ARB \n", + "6 Arab World ARB \n", + "7 Arab World ARB \n", + "8 Arab World ARB \n", + "9 Arab World ARB \n", + "10 Arab World ARB \n", + "11 Arab World ARB \n", + "12 Arab World ARB \n", + "13 Arab World ARB \n", + "14 Arab World ARB \n", + "15 Arab World ARB \n", + "16 Arab World ARB \n", + "17 Arab World ARB \n", + "18 Arab World ARB \n", + "19 Arab World ARB \n", + "20 Arab World ARB \n", + "21 Arab World ARB \n", + "22 Arab World ARB \n", + "23 Arab World ARB \n", + "24 Arab World ARB \n", + "25 Arab World ARB \n", + "26 Arab World ARB \n", + "27 Arab World ARB \n", + "28 Arab World ARB \n", + "29 Arab World ARB \n", + "... ... ... \n", + "5656428 Zimbabwe ZWE \n", + "5656429 Zimbabwe ZWE \n", + "5656430 Zimbabwe ZWE \n", + "5656431 Zimbabwe ZWE \n", + "5656432 Zimbabwe ZWE \n", + "5656433 Zimbabwe ZWE \n", + "5656434 Zimbabwe ZWE \n", + "5656435 Zimbabwe ZWE \n", + "5656436 Zimbabwe ZWE \n", + "5656437 Zimbabwe ZWE \n", + "5656438 Zimbabwe ZWE \n", + "5656439 Zimbabwe ZWE \n", + "5656440 Zimbabwe ZWE \n", + "5656441 Zimbabwe ZWE \n", + "5656442 Zimbabwe ZWE \n", + "5656443 Zimbabwe ZWE \n", + "5656444 Zimbabwe ZWE \n", + "5656445 Zimbabwe ZWE \n", + "5656446 Zimbabwe ZWE \n", + "5656447 Zimbabwe ZWE \n", + "5656448 Zimbabwe ZWE \n", + "5656449 Zimbabwe ZWE \n", + "5656450 Zimbabwe ZWE \n", + "5656451 Zimbabwe ZWE \n", + "5656452 Zimbabwe ZWE \n", + "5656453 Zimbabwe ZWE \n", + "5656454 Zimbabwe ZWE \n", + "5656455 Zimbabwe ZWE \n", + "5656456 Zimbabwe ZWE \n", + "5656457 Zimbabwe ZWE \n", + "\n", + " IndicatorName IndicatorCode \\\n", + "0 Adolescent fertility rate (births per 1,000 wo... SP.ADO.TFRT \n", + "1 Age dependency ratio (% of working-age populat... SP.POP.DPND \n", + "2 Age dependency ratio, old (% of working-age po... SP.POP.DPND.OL \n", + "3 Age dependency ratio, young (% of working-age ... SP.POP.DPND.YG \n", + "4 Arms exports (SIPRI trend indicator values) MS.MIL.XPRT.KD \n", + "5 Arms imports (SIPRI trend indicator values) MS.MIL.MPRT.KD \n", + "6 Birth rate, crude (per 1,000 people) SP.DYN.CBRT.IN \n", + "7 CO2 emissions (kt) EN.ATM.CO2E.KT \n", + "8 CO2 emissions (metric tons per capita) EN.ATM.CO2E.PC \n", + "9 CO2 emissions from gaseous fuel consumption (%... EN.ATM.CO2E.GF.ZS \n", + "10 CO2 emissions from liquid fuel consumption (% ... EN.ATM.CO2E.LF.ZS \n", + "11 CO2 emissions from liquid fuel consumption (kt) EN.ATM.CO2E.LF.KT \n", + "12 CO2 emissions from solid fuel consumption (% o... EN.ATM.CO2E.SF.ZS \n", + "13 Death rate, crude (per 1,000 people) SP.DYN.CDRT.IN \n", + "14 Fertility rate, total (births per woman) SP.DYN.TFRT.IN \n", + "15 Fixed telephone subscriptions IT.MLT.MAIN \n", + "16 Fixed telephone subscriptions (per 100 people) IT.MLT.MAIN.P2 \n", + "17 Hospital beds (per 1,000 people) SH.MED.BEDS.ZS \n", + "18 International migrant stock (% of population) SM.POP.TOTL.ZS \n", + "19 International migrant stock, total SM.POP.TOTL \n", + "20 Life expectancy at birth, female (years) SP.DYN.LE00.FE.IN \n", + "21 Life expectancy at birth, male (years) SP.DYN.LE00.MA.IN \n", + "22 Life expectancy at birth, total (years) SP.DYN.LE00.IN \n", + "23 Merchandise exports (current US$) TX.VAL.MRCH.CD.WT \n", + "24 Merchandise exports by the reporting economy (... TX.VAL.MRCH.WL.CD \n", + "25 Merchandise exports by the reporting economy, ... TX.VAL.MRCH.RS.ZS \n", + "26 Merchandise exports to developing economies in... TX.VAL.MRCH.R1.ZS \n", + "27 Merchandise exports to developing economies in... TX.VAL.MRCH.R3.ZS \n", + "28 Merchandise exports to developing economies in... TX.VAL.MRCH.R4.ZS \n", + "29 Merchandise exports to developing economies in... TX.VAL.MRCH.R5.ZS \n", + "... ... ... \n", + "5656428 PPG, multilateral (INT, current US$) DT.INT.MLAT.CD \n", + "5656429 PPG, official creditors (AMT, current US$) DT.AMT.OFFT.CD \n", + "5656430 PPG, official creditors (DIS, current US$) DT.DIS.OFFT.CD \n", + "5656431 PPG, official creditors (INT, current US$) DT.INT.OFFT.CD \n", + "5656432 PPG, other private creditors (AMT, current US$) DT.AMT.PROP.CD \n", + "5656433 PPG, other private creditors (DIS, current US$) DT.DIS.PROP.CD \n", + "5656434 PPG, other private creditors (INT, current US$) DT.INT.PROP.CD \n", + "5656435 PPG, private creditors (AMT, current US$) DT.AMT.PRVT.CD \n", + "5656436 PPG, private creditors (DIS, current US$) DT.DIS.PRVT.CD \n", + "5656437 PPG, private creditors (INT, current US$) DT.INT.PRVT.CD \n", + "5656438 Principal repayments on external debt, long-te... DT.AMT.DLXF.CD \n", + "5656439 Principal repayments on external debt, private... DT.AMT.DPNG.CD \n", + "5656440 Private credit bureau coverage (% of adults) IC.CRD.PRVT.ZS \n", + "5656441 Procedures to build a warehouse (number) IC.WRH.PROC \n", + "5656442 Procedures to register property (number) IC.PRP.PROC \n", + "5656443 Profit tax (% of commercial profits) IC.TAX.PRFT.CP.ZS \n", + "5656444 Proportion of seats held by women in national ... SG.GEN.PARL.ZS \n", + "5656445 Public credit registry coverage (% of adults) IC.CRD.PUBL.ZS \n", + "5656446 Source data assessment of statistical capacity... IQ.SCI.SRCE \n", + "5656447 Start-up procedures to register a business (nu... IC.REG.PROC \n", + "5656448 Strength of legal rights index (0=weak to 12=s... IC.LGL.CRED.XQ \n", + "5656449 Tax payments (number) IC.TAX.PAYM \n", + "5656450 Time required to build a warehouse (days) IC.WRH.DURS \n", + "5656451 Time required to enforce a contract (days) IC.LGL.DURS \n", + "5656452 Time required to get electricity (days) IC.ELC.TIME \n", + "5656453 Time required to register property (days) IC.PRP.DURS \n", + "5656454 Time required to start a business (days) IC.REG.DURS \n", + "5656455 Time to prepare and pay taxes (hours) IC.TAX.DURS \n", + "5656456 Time to resolve insolvency (years) IC.ISV.DURS \n", + "5656457 Total tax rate (% of commercial profits) IC.TAX.TOTL.CP.ZS \n", + "\n", + " Year Value \n", + "0 1960 1.335609e+02 \n", + "1 1960 8.779760e+01 \n", + "2 1960 6.634579e+00 \n", + "3 1960 8.102333e+01 \n", + "4 1960 3.000000e+06 \n", + "5 1960 5.380000e+08 \n", + "6 1960 4.769789e+01 \n", + "7 1960 5.956399e+04 \n", + "8 1960 6.439635e-01 \n", + "9 1960 5.041292e+00 \n", + "10 1960 8.485147e+01 \n", + "11 1960 4.954171e+04 \n", + "12 1960 4.726981e+00 \n", + "13 1960 1.975445e+01 \n", + "14 1960 6.924027e+00 \n", + "15 1960 4.068330e+05 \n", + "16 1960 6.167006e-01 \n", + "17 1960 1.929622e+00 \n", + "18 1960 2.990637e+00 \n", + "19 1960 3.324685e+06 \n", + "20 1960 4.788325e+01 \n", + "21 1960 4.586295e+01 \n", + "22 1960 4.684706e+01 \n", + "23 1960 4.645919e+09 \n", + "24 1960 2.468800e+09 \n", + "25 1960 1.646954e+01 \n", + "26 1960 2.260207e+00 \n", + "27 1960 4.496111e-01 \n", + "28 1960 6.379618e+00 \n", + "29 1960 2.790830e+00 \n", + "... ... ... \n", + "5656428 2015 1.626000e+06 \n", + "5656429 2015 1.663930e+08 \n", + "5656430 2015 8.377700e+07 \n", + "5656431 2015 2.321300e+07 \n", + "5656432 2015 6.250000e+05 \n", + "5656433 2015 0.000000e+00 \n", + "5656434 2015 1.000000e+03 \n", + "5656435 2015 6.250000e+05 \n", + "5656436 2015 0.000000e+00 \n", + "5656437 2015 1.000000e+03 \n", + "5656438 2015 1.670180e+08 \n", + "5656439 2015 0.000000e+00 \n", + "5656440 2015 3.210000e+01 \n", + "5656441 2015 1.000000e+01 \n", + "5656442 2015 5.000000e+00 \n", + "5656443 2015 1.880000e+01 \n", + "5656444 2015 3.148148e+01 \n", + "5656445 2015 0.000000e+00 \n", + "5656446 2015 6.000000e+01 \n", + "5656447 2015 9.000000e+00 \n", + "5656448 2015 5.000000e+00 \n", + "5656449 2015 4.900000e+01 \n", + "5656450 2015 4.480000e+02 \n", + "5656451 2015 4.100000e+02 \n", + "5656452 2015 1.060000e+02 \n", + "5656453 2015 3.600000e+01 \n", + "5656454 2015 9.000000e+01 \n", + "5656455 2015 2.420000e+02 \n", + "5656456 2015 3.300000e+00 \n", + "5656457 2015 3.280000e+01 \n", + "\n", + "[5656458 rows x 6 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cv" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "df=cv[['IndicatorName','CountryName']]" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IndicatorNameCountryName
0Adolescent fertility rate (births per 1,000 wo...Arab World
1Age dependency ratio (% of working-age populat...Arab World
2Age dependency ratio, old (% of working-age po...Arab World
3Age dependency ratio, young (% of working-age ...Arab World
4Arms exports (SIPRI trend indicator values)Arab World
5Arms imports (SIPRI trend indicator values)Arab World
6Birth rate, crude (per 1,000 people)Arab World
7CO2 emissions (kt)Arab World
8CO2 emissions (metric tons per capita)Arab World
9CO2 emissions from gaseous fuel consumption (%...Arab World
10CO2 emissions from liquid fuel consumption (% ...Arab World
11CO2 emissions from liquid fuel consumption (kt)Arab World
12CO2 emissions from solid fuel consumption (% o...Arab World
13Death rate, crude (per 1,000 people)Arab World
14Fertility rate, total (births per woman)Arab World
15Fixed telephone subscriptionsArab World
16Fixed telephone subscriptions (per 100 people)Arab World
17Hospital beds (per 1,000 people)Arab World
18International migrant stock (% of population)Arab World
19International migrant stock, totalArab World
20Life expectancy at birth, female (years)Arab World
21Life expectancy at birth, male (years)Arab World
22Life expectancy at birth, total (years)Arab World
23Merchandise exports (current US$)Arab World
24Merchandise exports by the reporting economy (...Arab World
25Merchandise exports by the reporting economy, ...Arab World
26Merchandise exports to developing economies in...Arab World
27Merchandise exports to developing economies in...Arab World
28Merchandise exports to developing economies in...Arab World
29Merchandise exports to developing economies in...Arab World
.........
5656428PPG, multilateral (INT, current US$)Zimbabwe
5656429PPG, official creditors (AMT, current US$)Zimbabwe
5656430PPG, official creditors (DIS, current US$)Zimbabwe
5656431PPG, official creditors (INT, current US$)Zimbabwe
5656432PPG, other private creditors (AMT, current US$)Zimbabwe
5656433PPG, other private creditors (DIS, current US$)Zimbabwe
5656434PPG, other private creditors (INT, current US$)Zimbabwe
5656435PPG, private creditors (AMT, current US$)Zimbabwe
5656436PPG, private creditors (DIS, current US$)Zimbabwe
5656437PPG, private creditors (INT, current US$)Zimbabwe
5656438Principal repayments on external debt, long-te...Zimbabwe
5656439Principal repayments on external debt, private...Zimbabwe
5656440Private credit bureau coverage (% of adults)Zimbabwe
5656441Procedures to build a warehouse (number)Zimbabwe
5656442Procedures to register property (number)Zimbabwe
5656443Profit tax (% of commercial profits)Zimbabwe
5656444Proportion of seats held by women in national ...Zimbabwe
5656445Public credit registry coverage (% of adults)Zimbabwe
5656446Source data assessment of statistical capacity...Zimbabwe
5656447Start-up procedures to register a business (nu...Zimbabwe
5656448Strength of legal rights index (0=weak to 12=s...Zimbabwe
5656449Tax payments (number)Zimbabwe
5656450Time required to build a warehouse (days)Zimbabwe
5656451Time required to enforce a contract (days)Zimbabwe
5656452Time required to get electricity (days)Zimbabwe
5656453Time required to register property (days)Zimbabwe
5656454Time required to start a business (days)Zimbabwe
5656455Time to prepare and pay taxes (hours)Zimbabwe
5656456Time to resolve insolvency (years)Zimbabwe
5656457Total tax rate (% of commercial profits)Zimbabwe
\n", + "

5656458 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " IndicatorName CountryName\n", + "0 Adolescent fertility rate (births per 1,000 wo... Arab World\n", + "1 Age dependency ratio (% of working-age populat... Arab World\n", + "2 Age dependency ratio, old (% of working-age po... Arab World\n", + "3 Age dependency ratio, young (% of working-age ... Arab World\n", + "4 Arms exports (SIPRI trend indicator values) Arab World\n", + "5 Arms imports (SIPRI trend indicator values) Arab World\n", + "6 Birth rate, crude (per 1,000 people) Arab World\n", + "7 CO2 emissions (kt) Arab World\n", + "8 CO2 emissions (metric tons per capita) Arab World\n", + "9 CO2 emissions from gaseous fuel consumption (%... Arab World\n", + "10 CO2 emissions from liquid fuel consumption (% ... Arab World\n", + "11 CO2 emissions from liquid fuel consumption (kt) Arab World\n", + "12 CO2 emissions from solid fuel consumption (% o... Arab World\n", + "13 Death rate, crude (per 1,000 people) Arab World\n", + "14 Fertility rate, total (births per woman) Arab World\n", + "15 Fixed telephone subscriptions Arab World\n", + "16 Fixed telephone subscriptions (per 100 people) Arab World\n", + "17 Hospital beds (per 1,000 people) Arab World\n", + "18 International migrant stock (% of population) Arab World\n", + "19 International migrant stock, total Arab World\n", + "20 Life expectancy at birth, female (years) Arab World\n", + "21 Life expectancy at birth, male (years) Arab World\n", + "22 Life expectancy at birth, total (years) Arab World\n", + "23 Merchandise exports (current US$) Arab World\n", + "24 Merchandise exports by the reporting economy (... Arab World\n", + "25 Merchandise exports by the reporting economy, ... Arab World\n", + "26 Merchandise exports to developing economies in... Arab World\n", + "27 Merchandise exports to developing economies in... Arab World\n", + "28 Merchandise exports to developing economies in... Arab World\n", + "29 Merchandise exports to developing economies in... Arab World\n", + "... ... ...\n", + "5656428 PPG, multilateral (INT, current US$) Zimbabwe\n", + "5656429 PPG, official creditors (AMT, current US$) Zimbabwe\n", + "5656430 PPG, official creditors (DIS, current US$) Zimbabwe\n", + "5656431 PPG, official creditors (INT, current US$) Zimbabwe\n", + "5656432 PPG, other private creditors (AMT, current US$) Zimbabwe\n", + "5656433 PPG, other private creditors (DIS, current US$) Zimbabwe\n", + "5656434 PPG, other private creditors (INT, current US$) Zimbabwe\n", + "5656435 PPG, private creditors (AMT, current US$) Zimbabwe\n", + "5656436 PPG, private creditors (DIS, current US$) Zimbabwe\n", + "5656437 PPG, private creditors (INT, current US$) Zimbabwe\n", + "5656438 Principal repayments on external debt, long-te... Zimbabwe\n", + "5656439 Principal repayments on external debt, private... Zimbabwe\n", + "5656440 Private credit bureau coverage (% of adults) Zimbabwe\n", + "5656441 Procedures to build a warehouse (number) Zimbabwe\n", + "5656442 Procedures to register property (number) Zimbabwe\n", + "5656443 Profit tax (% of commercial profits) Zimbabwe\n", + "5656444 Proportion of seats held by women in national ... Zimbabwe\n", + "5656445 Public credit registry coverage (% of adults) Zimbabwe\n", + "5656446 Source data assessment of statistical capacity... Zimbabwe\n", + "5656447 Start-up procedures to register a business (nu... Zimbabwe\n", + "5656448 Strength of legal rights index (0=weak to 12=s... 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