{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 6, Part 1 - Data processing, Info Viz\n", "While we'll focus a lot of scientific viz of your sims, we can also take this opportunity to do some \"info viz\" which can provide context to your sci viz. Info viz is an important skill to know how to do as well!\n", "\n", "Let's take a step back and look at some larger planet data to see how we might process a large list of data. In this example, we'll use more Kepler data.\n", "\n", "While filtering is important only sometimes for simulated data, it is generally necessary for observational data & if we want to compare our simulations to observations.\n", "\n", "We'll use the \"pandas\" package to do this which can be a useful thing to know how to use anyway!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# import our usual stuffs\n", "%matplotlib inline\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# now, import pandas\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# now let's read in the kepler confirmed planets dataset\n", "planets = pd.read_csv('https://jnaiman.github.io/csci-p-14110/lesson06/data/planets_2019.07.12_17.16.25.csv', \n", " sep=\",\", comment=\"#\")\n", "#note: feel free to download this and read from your download as well" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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111 UMib11 UMi bRadial Velocity01516.2199703.200000-3.2000000.0...NaN0.0NaNNaNNaN0.0NaNNaNNaN0.0
214 Andb14 And bRadial Velocity01185.8400000.230000-0.2300000.0...NaN0.01.763NaNNaN0.0NaNNaNNaNNaN
314 Herb14 Her bRadial Velocity011773.4000202.500000-2.5000000.0...NaN0.0NaNNaNNaN0.0NaNNaNNaN0.0
416 Cyg Bb16 Cyg B bRadial Velocity01798.5000001.000000-1.0000000.0...NaN0.0NaNNaNNaN0.0NaNNaNNaN0.0
518 Delb18 Del bRadial Velocity01993.3000003.200000-3.2000000.0...NaN0.01.602NaNNaN0.0NaNNaNNaNNaN
61RXS J160929.1-210524b1RXS J160929.1-210524 bImaging01NaNNaNNaNNaN...NaN0.0-0.3700.150-0.1500.00.005NaNNaNNaN
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184 UMab4 UMa bRadial Velocity01269.3000001.960000-1.9600000.0...NaN0.0NaNNaNNaNNaN4.6042.000-2.000NaN
1942 Drab42 Dra bRadial Velocity01479.1000006.200000-6.2000000.0...NaN0.0NaNNaNNaNNaN9.4901.760-1.760NaN
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2147 UMac47 UMa cRadial Velocity032391.000000100.000000-87.0000000.0...NaN0.0NaNNaNNaNNaN7.3001.900-1.900NaN
2247 UMad47 UMa dRadial Velocity0314002.0000004018.000000-5095.0000000.0...NaN0.0NaNNaNNaNNaN7.3001.900-1.900NaN
2351 Erib51 Eri bImaging0111688.0000006209.250000-3287.2500000.0...NaN0.00.8500.060-0.0600.00.0200.006-0.006NaN
2451 Pegb51 Peg bRadial Velocity014.2307850.000036-0.0000360.0...NaN0.0NaNNaNNaNNaN4.0002.500-2.500NaN
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2655 Cncc55 Cnc cRadial Velocity0544.4175000.007300-0.0073000.0...NaN0.0-0.2350.010-0.0110.010.2002.500-2.500NaN
2755 Cncd55 Cnc dRadial Velocity054825.00000039.000000-39.0000000.0...NaN0.0-0.2350.010-0.0110.010.2002.500-2.500NaN
2855 Cnce55 Cnc eRadial Velocity050.7365390.000007-0.0000070.0...NaN0.0-0.2350.010-0.0110.010.2002.500-2.500NaN
2955 Cncf55 Cnc fRadial Velocity05262.0000000.510000-0.5100000.0...NaN0.0-0.2350.010-0.0110.010.2002.500-2.500NaN
..................................................................
3986eps CrBbeps CrB bRadial Velocity01417.9000000.500000-0.5000000.0...NaN0.0NaNNaNNaN0.01.7400.370-0.370NaN
3987eps Eribeps Eri bRadial Velocity012502.00000010.000000-10.0000000.0...NaN0.0NaNNaNNaNNaN0.800NaNNaNNaN
3988eps Taubeps Tau bRadial Velocity01594.9000005.300000-5.3000000.0...NaN0.01.9870.037NaN0.0NaNNaNNaNNaN
3989gam 1 Leobgam 1 Leo bRadial Velocity01428.5000001.250000-1.2500000.0...NaN0.0NaNNaNNaNNaNNaNNaNNaNNaN
3990gam Cepbgam Cep bRadial Velocity01903.3000001.500000-1.5000000.0...NaN0.0NaNNaNNaNNaNNaNNaNNaNNaN
3991gam Libbgam Lib bRadial Velocity02415.2000001.800000-1.9000000.0...NaN0.01.8500.035-0.0380.02.8201.970-0.8200.0
3992gam Libcgam Lib cRadial Velocity02964.6000003.100000-3.1000000.0...NaN0.01.8500.035-0.0380.02.8201.970-0.8200.0
3993iot Drabiot Dra bRadial Velocity01511.0980000.089000-0.0890000.0...NaN0.0NaNNaNNaNNaNNaNNaNNaNNaN
3994kap Andbkap And bImaging01NaNNaNNaNNaN...NaN0.01.8300.040-0.0400.00.0300.120-0.010NaN
3995kap CrBbkap CrB bRadial Velocity011285.00000014.000000-14.0000000.0...NaN0.0NaNNaNNaN0.0NaNNaNNaN0.0
3996mu Leobmu Leo bRadial Velocity01357.8000001.200000-1.2000000.0...NaN0.01.797NaNNaN0.03.3500.700-0.700NaN
3997nu Ophbnu Oph bRadial Velocity02530.3200000.350000-0.3500000.0...NaN0.02.090NaNNaN0.0NaNNaNNaNNaN
3998nu Ophcnu Oph cRadial Velocity023186.00000014.000000-14.0000000.0...NaN0.02.090NaNNaN0.0NaNNaNNaNNaN
3999ome Serbome Ser bRadial Velocity01277.0200000.520000-0.5100000.0...NaN0.01.850NaNNaN0.0NaNNaNNaNNaN
4000omi CrBbomi CrB bRadial Velocity01187.8300000.540000-0.5400000.0...NaN0.01.709NaNNaN0.0NaNNaNNaNNaN
4001omi UMabomi UMa bRadial Velocity011630.00000035.000000-35.0000000.0...NaN0.02.140NaNNaN0.0NaNNaNNaNNaN
4002HD 39091cpi Men cTransit026.2679000.000460-0.0004600.0...NaN0.00.1600.006-0.0060.02.9801.400-1.3000.0
4003psi 1 Dra Bbpsi 1 Dra B bRadial Velocity013117.00000042.000000-42.0000000.0...NaN0.0NaNNaNNaNNaN3.3001.000-1.000NaN
4004rho CrBbrho CrB bRadial Velocity1239.8458000.001500-0.0014000.0...NaN0.00.2320.011-0.0110.0NaNNaNNaNNaN
4005rho CrBcrho CrB cRadial Velocity02102.5400000.170000-0.1700000.0...NaN0.00.2320.011-0.0110.0NaNNaNNaNNaN
4006tau Boobtau Boo bRadial Velocity013.3124570.000007-0.0000070.0...NaN0.0NaNNaNNaNNaNNaNNaNNaNNaN
4007tau Cetetau Cet eRadial Velocity04162.8700001.080000-0.4600000.0...NaN0.0NaNNaNNaN0.0NaNNaNNaNNaN
4008tau Cetftau Cet fRadial Velocity04636.13000011.700000-47.6900000.0...NaN0.0NaNNaNNaN0.0NaNNaNNaNNaN
4009tau Cetgtau Cet gRadial Velocity0420.0000000.020000-0.0100000.0...NaN0.0NaNNaNNaN0.0NaNNaNNaNNaN
4010tau Cethtau Cet hRadial Velocity0449.4100000.080000-0.1000000.0...NaN0.0NaNNaNNaN0.0NaNNaNNaNNaN
4011tau Gembtau Gem bRadial Velocity01305.5000000.100000-0.1000000.0...NaN0.0NaNNaNNaNNaN1.2200.760-0.760NaN
4012ups Andbups And bRadial Velocity034.6170330.000023-0.0000230.0...NaN0.0NaNNaNNaNNaN5.000NaNNaNNaN
4013ups Andcups And cRadial Velocity03241.2580000.064000-0.0640000.0...NaN0.0NaNNaNNaNNaN5.000NaNNaNNaN
4014ups Anddups And dRadial Velocity031276.4600000.570000-0.5700000.0...NaN0.0NaNNaNNaNNaN5.000NaNNaNNaN
4015xi Aqlbxi Aql bRadial Velocity01136.7500000.250000-0.2500000.0...NaN0.01.839NaNNaN0.0NaNNaNNaNNaN
\n", "

4016 rows × 88 columns

\n", "
" ], "text/plain": [ " pl_hostname pl_letter pl_name \\\n", "0 11 Com b 11 Com b \n", "1 11 UMi b 11 UMi b \n", "2 14 And b 14 And b \n", "3 14 Her b 14 Her b \n", "4 16 Cyg B b 16 Cyg B b \n", "5 18 Del b 18 Del b \n", "6 1RXS J160929.1-210524 b 1RXS J160929.1-210524 b \n", "7 24 Boo b 24 Boo b \n", "8 24 Sex b 24 Sex b \n", "9 24 Sex c 24 Sex c \n", "10 2MASS J01225093-2439505 b 2MASS J01225093-2439505 b \n", "11 2MASS J02192210-3925225 b 2MASS J02192210-3925225 b \n", "12 2MASS J04414489+2301513 b 2MASS J04414489+2301513 b \n", "13 2MASS J12073346-3932539 b 2MASS J12073346-3932539 b \n", "14 2MASS J19383260+4603591 b 2MASS J19383260+4603591 b \n", "15 2MASS J21402931+1625183 A b 2MASS J21402931+1625183 A b \n", "16 2MASS J22362452+4751425 b 2MASS J22362452+4751425 b \n", "17 30 Ari B b 30 Ari B b \n", "18 4 UMa b 4 UMa b \n", "19 42 Dra b 42 Dra b \n", "20 47 UMa b 47 UMa b \n", "21 47 UMa c 47 UMa c \n", "22 47 UMa d 47 UMa d \n", "23 51 Eri b 51 Eri b \n", "24 51 Peg b 51 Peg b \n", "25 55 Cnc b 55 Cnc b \n", "26 55 Cnc c 55 Cnc c \n", "27 55 Cnc d 55 Cnc d \n", "28 55 Cnc e 55 Cnc e \n", "29 55 Cnc f 55 Cnc f \n", "... ... ... ... \n", "3986 eps CrB b eps CrB b \n", "3987 eps Eri b eps Eri b \n", "3988 eps Tau b eps Tau b \n", "3989 gam 1 Leo b gam 1 Leo b \n", "3990 gam Cep b gam Cep b \n", "3991 gam Lib b gam Lib b \n", "3992 gam Lib c gam Lib c \n", "3993 iot Dra b iot Dra b \n", "3994 kap And b kap And b \n", "3995 kap CrB b kap CrB b \n", "3996 mu Leo b mu Leo b \n", "3997 nu Oph b nu Oph b \n", "3998 nu Oph c nu Oph c \n", "3999 ome Ser b ome Ser b \n", "4000 omi CrB b omi CrB b \n", "4001 omi UMa b omi UMa b \n", "4002 HD 39091 c pi Men c \n", "4003 psi 1 Dra B b psi 1 Dra B b \n", "4004 rho CrB b rho CrB b \n", "4005 rho CrB c rho CrB c \n", "4006 tau Boo b tau Boo b \n", "4007 tau Cet e tau Cet e \n", "4008 tau Cet f tau Cet f \n", "4009 tau Cet g tau Cet g \n", "4010 tau Cet h tau Cet h \n", "4011 tau Gem b tau Gem b \n", "4012 ups And b ups And b \n", "4013 ups And c ups And c \n", "4014 ups And d ups And d \n", "4015 xi Aql b xi Aql b \n", "\n", " pl_discmethod pl_controvflag pl_pnum pl_orbper \\\n", "0 Radial Velocity 0 1 326.030000 \n", "1 Radial Velocity 0 1 516.219970 \n", "2 Radial Velocity 0 1 185.840000 \n", "3 Radial Velocity 0 1 1773.400020 \n", "4 Radial Velocity 0 1 798.500000 \n", "5 Radial Velocity 0 1 993.300000 \n", "6 Imaging 0 1 NaN \n", "7 Radial Velocity 0 1 30.350600 \n", "8 Radial Velocity 0 2 452.800000 \n", "9 Radial Velocity 0 2 883.000000 \n", "10 Imaging 0 1 NaN \n", "11 Imaging 0 1 NaN \n", "12 Imaging 0 1 NaN \n", "13 Imaging 0 1 NaN \n", "14 Eclipse Timing Variations 0 1 416.000000 \n", "15 Imaging 0 1 7336.500000 \n", "16 Imaging 0 1 NaN \n", "17 Radial Velocity 0 1 335.100010 \n", "18 Radial Velocity 0 1 269.300000 \n", "19 Radial Velocity 0 1 479.100000 \n", "20 Radial Velocity 0 3 1078.000000 \n", "21 Radial Velocity 0 3 2391.000000 \n", "22 Radial Velocity 0 3 14002.000000 \n", "23 Imaging 0 1 11688.000000 \n", "24 Radial Velocity 0 1 4.230785 \n", "25 Radial Velocity 0 5 14.651520 \n", "26 Radial Velocity 0 5 44.417500 \n", "27 Radial Velocity 0 5 4825.000000 \n", "28 Radial Velocity 0 5 0.736539 \n", "29 Radial Velocity 0 5 262.000000 \n", "... ... ... ... ... \n", "3986 Radial Velocity 0 1 417.900000 \n", "3987 Radial Velocity 0 1 2502.000000 \n", "3988 Radial Velocity 0 1 594.900000 \n", "3989 Radial Velocity 0 1 428.500000 \n", "3990 Radial Velocity 0 1 903.300000 \n", "3991 Radial Velocity 0 2 415.200000 \n", "3992 Radial Velocity 0 2 964.600000 \n", "3993 Radial Velocity 0 1 511.098000 \n", "3994 Imaging 0 1 NaN \n", "3995 Radial Velocity 0 1 1285.000000 \n", "3996 Radial Velocity 0 1 357.800000 \n", "3997 Radial Velocity 0 2 530.320000 \n", "3998 Radial Velocity 0 2 3186.000000 \n", "3999 Radial Velocity 0 1 277.020000 \n", "4000 Radial Velocity 0 1 187.830000 \n", "4001 Radial Velocity 0 1 1630.000000 \n", "4002 Transit 0 2 6.267900 \n", "4003 Radial Velocity 0 1 3117.000000 \n", "4004 Radial Velocity 1 2 39.845800 \n", "4005 Radial Velocity 0 2 102.540000 \n", "4006 Radial Velocity 0 1 3.312457 \n", "4007 Radial Velocity 0 4 162.870000 \n", "4008 Radial Velocity 0 4 636.130000 \n", "4009 Radial Velocity 0 4 20.000000 \n", "4010 Radial Velocity 0 4 49.410000 \n", "4011 Radial Velocity 0 1 305.500000 \n", "4012 Radial Velocity 0 3 4.617033 \n", "4013 Radial Velocity 0 3 241.258000 \n", "4014 Radial Velocity 0 3 1276.460000 \n", "4015 Radial Velocity 0 1 136.750000 \n", "\n", " pl_orbpererr1 pl_orbpererr2 pl_orbperlim ... st_sperr st_splim \\\n", "0 0.320000 -0.320000 0.0 ... NaN 0.0 \n", "1 3.200000 -3.200000 0.0 ... NaN 0.0 \n", "2 0.230000 -0.230000 0.0 ... NaN 0.0 \n", "3 2.500000 -2.500000 0.0 ... NaN 0.0 \n", "4 1.000000 -1.000000 0.0 ... NaN 0.0 \n", "5 3.200000 -3.200000 0.0 ... NaN 0.0 \n", "6 NaN NaN NaN ... NaN 0.0 \n", "7 0.007800 -0.007700 0.0 ... NaN 0.0 \n", "8 2.100000 -4.500000 0.0 ... NaN 0.0 \n", "9 32.400000 -13.800000 0.0 ... NaN 0.0 \n", "10 NaN NaN NaN ... NaN 0.0 \n", "11 NaN NaN NaN ... NaN 0.0 \n", "12 NaN NaN NaN ... NaN 0.0 \n", "13 NaN NaN NaN ... NaN 0.0 \n", "14 2.000000 -2.000000 0.0 ... NaN NaN \n", "15 1934.500000 -584.000000 0.0 ... NaN 0.0 \n", "16 NaN NaN NaN ... NaN 0.0 \n", "17 2.500000 -2.500000 0.0 ... NaN 0.0 \n", "18 1.960000 -1.960000 0.0 ... NaN 0.0 \n", "19 6.200000 -6.200000 0.0 ... NaN 0.0 \n", "20 2.000000 -2.000000 0.0 ... NaN 0.0 \n", "21 100.000000 -87.000000 0.0 ... NaN 0.0 \n", "22 4018.000000 -5095.000000 0.0 ... NaN 0.0 \n", "23 6209.250000 -3287.250000 0.0 ... NaN 0.0 \n", "24 0.000036 -0.000036 0.0 ... NaN 0.0 \n", "25 0.000150 -0.000150 0.0 ... NaN 0.0 \n", "26 0.007300 -0.007300 0.0 ... NaN 0.0 \n", "27 39.000000 -39.000000 0.0 ... NaN 0.0 \n", "28 0.000007 -0.000007 0.0 ... NaN 0.0 \n", "29 0.510000 -0.510000 0.0 ... NaN 0.0 \n", "... ... ... ... ... ... ... \n", "3986 0.500000 -0.500000 0.0 ... NaN 0.0 \n", "3987 10.000000 -10.000000 0.0 ... NaN 0.0 \n", "3988 5.300000 -5.300000 0.0 ... NaN 0.0 \n", "3989 1.250000 -1.250000 0.0 ... NaN 0.0 \n", "3990 1.500000 -1.500000 0.0 ... NaN 0.0 \n", "3991 1.800000 -1.900000 0.0 ... NaN 0.0 \n", "3992 3.100000 -3.100000 0.0 ... NaN 0.0 \n", "3993 0.089000 -0.089000 0.0 ... NaN 0.0 \n", "3994 NaN NaN NaN ... NaN 0.0 \n", "3995 14.000000 -14.000000 0.0 ... NaN 0.0 \n", "3996 1.200000 -1.200000 0.0 ... NaN 0.0 \n", "3997 0.350000 -0.350000 0.0 ... NaN 0.0 \n", "3998 14.000000 -14.000000 0.0 ... NaN 0.0 \n", "3999 0.520000 -0.510000 0.0 ... NaN 0.0 \n", "4000 0.540000 -0.540000 0.0 ... NaN 0.0 \n", "4001 35.000000 -35.000000 0.0 ... NaN 0.0 \n", "4002 0.000460 -0.000460 0.0 ... NaN 0.0 \n", "4003 42.000000 -42.000000 0.0 ... NaN 0.0 \n", "4004 0.001500 -0.001400 0.0 ... NaN 0.0 \n", "4005 0.170000 -0.170000 0.0 ... NaN 0.0 \n", "4006 0.000007 -0.000007 0.0 ... NaN 0.0 \n", "4007 1.080000 -0.460000 0.0 ... NaN 0.0 \n", "4008 11.700000 -47.690000 0.0 ... NaN 0.0 \n", "4009 0.020000 -0.010000 0.0 ... NaN 0.0 \n", "4010 0.080000 -0.100000 0.0 ... NaN 0.0 \n", "4011 0.100000 -0.100000 0.0 ... NaN 0.0 \n", "4012 0.000023 -0.000023 0.0 ... NaN 0.0 \n", "4013 0.064000 -0.064000 0.0 ... NaN 0.0 \n", "4014 0.570000 -0.570000 0.0 ... NaN 0.0 \n", "4015 0.250000 -0.250000 0.0 ... NaN 0.0 \n", "\n", " st_lum st_lumerr1 st_lumerr2 st_lumlim st_age st_ageerr1 \\\n", "0 2.243 0.071 -0.085 0.0 NaN NaN \n", "1 NaN NaN NaN 0.0 NaN NaN \n", "2 1.763 NaN NaN 0.0 NaN NaN \n", "3 NaN NaN NaN 0.0 NaN NaN \n", "4 NaN NaN NaN 0.0 NaN NaN \n", "5 1.602 NaN NaN 0.0 NaN NaN \n", "6 -0.370 0.150 -0.150 0.0 0.005 NaN \n", "7 1.774 0.047 -0.053 0.0 6.920 4.830 \n", "8 1.164 0.003 -0.003 0.0 2.700 0.400 \n", "9 1.164 0.003 -0.003 0.0 2.700 0.400 \n", "10 -1.720 0.110 -0.110 0.0 0.120 0.010 \n", "11 -2.230 0.060 -0.060 0.0 0.035 0.005 \n", "12 NaN NaN NaN NaN 0.001 NaN \n", "13 NaN NaN NaN NaN 0.008 0.004 \n", "14 NaN NaN NaN NaN NaN NaN \n", "15 -3.480 0.350 -0.350 0.0 NaN NaN \n", "16 NaN NaN NaN NaN 0.120 0.010 \n", "17 NaN NaN NaN 0.0 NaN NaN \n", "18 NaN NaN NaN NaN 4.604 2.000 \n", "19 NaN NaN NaN NaN 9.490 1.760 \n", "20 NaN NaN NaN NaN 7.300 1.900 \n", "21 NaN NaN NaN NaN 7.300 1.900 \n", "22 NaN NaN NaN NaN 7.300 1.900 \n", "23 0.850 0.060 -0.060 0.0 0.020 0.006 \n", "24 NaN NaN NaN NaN 4.000 2.500 \n", "25 -0.235 0.010 -0.011 0.0 10.200 2.500 \n", "26 -0.235 0.010 -0.011 0.0 10.200 2.500 \n", "27 -0.235 0.010 -0.011 0.0 10.200 2.500 \n", "28 -0.235 0.010 -0.011 0.0 10.200 2.500 \n", "29 -0.235 0.010 -0.011 0.0 10.200 2.500 \n", "... ... ... ... ... ... ... \n", "3986 NaN NaN NaN 0.0 1.740 0.370 \n", "3987 NaN NaN NaN NaN 0.800 NaN \n", "3988 1.987 0.037 NaN 0.0 NaN NaN \n", "3989 NaN NaN NaN NaN NaN NaN \n", "3990 NaN NaN NaN NaN NaN NaN \n", "3991 1.850 0.035 -0.038 0.0 2.820 1.970 \n", "3992 1.850 0.035 -0.038 0.0 2.820 1.970 \n", "3993 NaN NaN NaN NaN NaN NaN \n", "3994 1.830 0.040 -0.040 0.0 0.030 0.120 \n", "3995 NaN NaN NaN 0.0 NaN NaN \n", "3996 1.797 NaN NaN 0.0 3.350 0.700 \n", "3997 2.090 NaN NaN 0.0 NaN NaN \n", "3998 2.090 NaN NaN 0.0 NaN NaN \n", "3999 1.850 NaN NaN 0.0 NaN NaN \n", "4000 1.709 NaN NaN 0.0 NaN NaN \n", "4001 2.140 NaN NaN 0.0 NaN NaN \n", "4002 0.160 0.006 -0.006 0.0 2.980 1.400 \n", "4003 NaN NaN NaN NaN 3.300 1.000 \n", "4004 0.232 0.011 -0.011 0.0 NaN NaN \n", "4005 0.232 0.011 -0.011 0.0 NaN NaN \n", "4006 NaN NaN NaN NaN NaN NaN \n", "4007 NaN NaN NaN 0.0 NaN NaN \n", "4008 NaN NaN NaN 0.0 NaN NaN \n", "4009 NaN NaN NaN 0.0 NaN NaN \n", "4010 NaN NaN NaN 0.0 NaN NaN \n", "4011 NaN NaN NaN NaN 1.220 0.760 \n", "4012 NaN NaN NaN NaN 5.000 NaN \n", "4013 NaN NaN NaN NaN 5.000 NaN \n", "4014 NaN NaN NaN NaN 5.000 NaN \n", "4015 1.839 NaN NaN 0.0 NaN NaN \n", "\n", " st_ageerr2 st_agelim \n", "0 NaN NaN \n", "1 NaN 0.0 \n", "2 NaN NaN \n", "3 NaN 0.0 \n", "4 NaN 0.0 \n", "5 NaN NaN \n", "6 NaN NaN \n", "7 -2.750 0.0 \n", "8 -0.400 NaN \n", "9 -0.400 NaN \n", "10 -0.010 NaN \n", "11 -0.005 NaN \n", "12 NaN NaN \n", "13 -0.003 NaN \n", "14 NaN NaN \n", "15 NaN NaN \n", "16 -0.010 NaN \n", "17 NaN 0.0 \n", "18 -2.000 NaN \n", "19 -1.760 NaN \n", "20 -1.900 NaN \n", "21 -1.900 NaN \n", "22 -1.900 NaN \n", "23 -0.006 NaN \n", "24 -2.500 NaN \n", "25 -2.500 NaN \n", "26 -2.500 NaN \n", "27 -2.500 NaN \n", "28 -2.500 NaN \n", "29 -2.500 NaN \n", "... ... ... \n", "3986 -0.370 NaN \n", "3987 NaN NaN \n", "3988 NaN NaN \n", "3989 NaN NaN \n", "3990 NaN NaN \n", "3991 -0.820 0.0 \n", "3992 -0.820 0.0 \n", "3993 NaN NaN \n", "3994 -0.010 NaN \n", "3995 NaN 0.0 \n", "3996 -0.700 NaN \n", "3997 NaN NaN \n", "3998 NaN NaN \n", "3999 NaN NaN \n", "4000 NaN NaN \n", "4001 NaN NaN \n", "4002 -1.300 0.0 \n", "4003 -1.000 NaN \n", "4004 NaN NaN \n", "4005 NaN NaN \n", "4006 NaN NaN \n", "4007 NaN NaN \n", "4008 NaN NaN \n", "4009 NaN NaN \n", "4010 NaN NaN \n", "4011 -0.760 NaN \n", "4012 NaN NaN \n", "4013 NaN NaN \n", "4014 NaN NaN \n", "4015 NaN NaN \n", "\n", "[4016 rows x 88 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "planets\n", "# formatting here is sort of nice" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# skip this\n", "# how many entries are there? as an iterable\n", "#planets.index" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pl_hostnamepl_letterpl_namepl_discmethodpl_controvflagpl_pnumpl_orbperpl_orbpererr1pl_orbpererr2pl_orbperlim...st_sperrst_splimst_lumst_lumerr1st_lumerr2st_lumlimst_agest_ageerr1st_ageerr2st_agelim
011 Comb11 Com bRadial Velocity01326.030000.32-0.320.0...NaN0.02.2430.071-0.0850.0NaNNaNNaNNaN
111 UMib11 UMi bRadial Velocity01516.219973.20-3.200.0...NaN0.0NaNNaNNaN0.0NaNNaNNaN0.0
214 Andb14 And bRadial Velocity01185.840000.23-0.230.0...NaN0.01.763NaNNaN0.0NaNNaNNaNNaN
314 Herb14 Her bRadial Velocity011773.400022.50-2.500.0...NaN0.0NaNNaNNaN0.0NaNNaNNaN0.0
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4 rows × 88 columns

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" ], "text/plain": [ " pl_hostname pl_letter pl_name pl_discmethod pl_controvflag pl_pnum \\\n", "0 11 Com b 11 Com b Radial Velocity 0 1 \n", "1 11 UMi b 11 UMi b Radial Velocity 0 1 \n", "2 14 And b 14 And b Radial Velocity 0 1 \n", "3 14 Her b 14 Her b Radial Velocity 0 1 \n", "\n", " pl_orbper pl_orbpererr1 pl_orbpererr2 pl_orbperlim ... st_sperr \\\n", "0 326.03000 0.32 -0.32 0.0 ... NaN \n", "1 516.21997 3.20 -3.20 0.0 ... NaN \n", "2 185.84000 0.23 -0.23 0.0 ... NaN \n", "3 1773.40002 2.50 -2.50 0.0 ... NaN \n", "\n", " st_splim st_lum st_lumerr1 st_lumerr2 st_lumlim st_age st_ageerr1 \\\n", "0 0.0 2.243 0.071 -0.085 0.0 NaN NaN \n", "1 0.0 NaN NaN NaN 0.0 NaN NaN \n", "2 0.0 1.763 NaN NaN 0.0 NaN NaN \n", "3 0.0 NaN NaN NaN 0.0 NaN NaN \n", "\n", " st_ageerr2 st_agelim \n", "0 NaN NaN \n", "1 NaN 0.0 \n", "2 NaN NaN \n", "3 NaN 0.0 \n", "\n", "[4 rows x 88 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "planets.loc[0:3] #easy to grab subsets - here by label\n", "#planets.loc? #easy to grab subsets - here by label" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['pl_hostname', 'pl_letter', 'pl_name', 'pl_discmethod',\n", " 'pl_controvflag', 'pl_pnum', 'pl_orbper', 'pl_orbpererr1',\n", " 'pl_orbpererr2', 'pl_orbperlim', 'pl_orbsmax', 'pl_orbsmaxerr1',\n", " 'pl_orbsmaxerr2', 'pl_orbsmaxlim', 'pl_orbeccen', 'pl_orbeccenerr1',\n", " 'pl_orbeccenerr2', 'pl_orbeccenlim', 'pl_orbincl', 'pl_orbinclerr1',\n", " 'pl_orbinclerr2', 'pl_orbincllim', 'pl_bmassj', 'pl_bmassjerr1',\n", " 'pl_bmassjerr2', 'pl_bmassjlim', 'pl_bmassprov', 'pl_radj',\n", " 'pl_radjerr1', 'pl_radjerr2', 'pl_radjlim', 'pl_dens', 'pl_denserr1',\n", " 'pl_denserr2', 'pl_denslim', 'ra_str', 'ra', 'dec_str', 'dec',\n", " 'st_dist', 'st_disterr1', 'st_disterr2', 'st_distlim', 'gaia_dist',\n", " 'gaia_disterr1', 'gaia_disterr2', 'gaia_distlim', 'st_optmag',\n", " 'st_optmagerr', 'st_optmaglim', 'st_optband', 'gaia_gmag',\n", " 'gaia_gmagerr', 'gaia_gmaglim', 'st_teff', 'st_tefferr1', 'st_tefferr2',\n", " 'st_tefflim', 'st_mass', 'st_masserr1', 'st_masserr2', 'st_masslim',\n", " 'st_rad', 'st_raderr1', 'st_raderr2', 'st_radlim', 'pl_massj',\n", " 'pl_massjerr1', 'pl_massjerr2', 'pl_massjlim', 'pl_rade', 'pl_radeerr1',\n", " 'pl_radeerr2', 'pl_radelim', 'pl_disc', 'pl_mnum', 'st_sp', 'st_spstr',\n", " 'st_sperr', 'st_splim', 'st_lum', 'st_lumerr1', 'st_lumerr2',\n", " 'st_lumlim', 'st_age', 'st_ageerr1', 'st_ageerr2', 'st_agelim'],\n", " dtype='object')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "planets.columns\n", "# names of columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# skip\n", "#planets.loc[0:10][\"pl_orbeccen\"] # grab 1-10 entries, and print out the eccentricites of those entries\n", "# notice there are some NaN's -> these just don't have entries" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['11 Com', '11 UMi', '14 And', ..., 'tau Gem', 'ups And', 'xi Aql'],\n", " dtype=object)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# what are the names of the unique host stars?\n", "planets[\"pl_hostname\"].unique() " ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2994" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "planets[\"pl_hostname\"].nunique() # how many unique host stars?" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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pl_controvflagpl_pnumpl_orbperpl_orbpererr1pl_orbpererr2pl_orbperlimpl_orbsmaxpl_orbsmaxerr1pl_orbsmaxerr2pl_orbsmaxlim...st_sperrst_splimst_lumst_lumerr1st_lumerr2st_lumlimst_agest_ageerr1st_ageerr2st_agelim
count4016.0000004016.0000003.907000e+033.775000e+033.775000e+033941.0000002338.0000001535.0000001534.0000002501.000000...0.01335.0584.000000506.000000493.0000001401.02004.0000001865.0000001865.000000904.0
mean0.0027391.7724102.326363e+031.051761e+03-1.082432e+03-0.0005076.7028670.424368-0.429475-0.000400...NaN0.0-0.1365290.067524-0.0752880.04.1613212.991969-1.9475770.0
std0.0522711.1595061.171632e+055.948289e+045.968316e+040.03902081.2798635.7155575.9960490.019996...NaN0.01.1354860.0903810.1280680.02.1623732.1172591.0874340.0
min0.0000001.0000009.070629e-020.000000e+00-3.650000e+06-1.0000000.0044000.000000-200.000000-1.000000...NaN0.0-3.4800000.000000-1.4310000.00.0010000.001000-8.0000000.0
25%0.0000001.0000004.516936e+001.600000e-05-1.165000e-030.0000000.0570000.000800-0.0400000.000000...NaN0.0-0.6800000.027000-0.0770000.02.9500001.140000-2.7100000.0
50%0.0000001.0000001.193212e+019.500000e-05-9.600000e-050.0000000.1188400.003100-0.0031000.000000...NaN0.0-0.0245000.049000-0.0500000.04.0700002.500000-2.0200000.0
75%0.0000002.0000004.231980e+011.173500e-03-1.600000e-050.0000000.6800000.040000-0.0008080.000000...NaN0.00.4790000.079000-0.0280000.04.7900004.890000-1.0300000.0
max1.0000008.0000007.300000e+063.650000e+060.000000e+001.0000002500.000000200.0000000.0000000.000000...NaN0.03.0150000.8450000.0000000.023.00000026.000000-0.0010000.0
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8 rows × 79 columns

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" ], "text/plain": [ " pl_controvflag pl_pnum pl_orbper pl_orbpererr1 \\\n", "count 4016.000000 4016.000000 3.907000e+03 3.775000e+03 \n", "mean 0.002739 1.772410 2.326363e+03 1.051761e+03 \n", "std 0.052271 1.159506 1.171632e+05 5.948289e+04 \n", "min 0.000000 1.000000 9.070629e-02 0.000000e+00 \n", "25% 0.000000 1.000000 4.516936e+00 1.600000e-05 \n", "50% 0.000000 1.000000 1.193212e+01 9.500000e-05 \n", "75% 0.000000 2.000000 4.231980e+01 1.173500e-03 \n", "max 1.000000 8.000000 7.300000e+06 3.650000e+06 \n", "\n", " pl_orbpererr2 pl_orbperlim pl_orbsmax pl_orbsmaxerr1 \\\n", "count 3.775000e+03 3941.000000 2338.000000 1535.000000 \n", "mean -1.082432e+03 -0.000507 6.702867 0.424368 \n", "std 5.968316e+04 0.039020 81.279863 5.715557 \n", "min -3.650000e+06 -1.000000 0.004400 0.000000 \n", "25% -1.165000e-03 0.000000 0.057000 0.000800 \n", "50% -9.600000e-05 0.000000 0.118840 0.003100 \n", "75% -1.600000e-05 0.000000 0.680000 0.040000 \n", "max 0.000000e+00 1.000000 2500.000000 200.000000 \n", "\n", " pl_orbsmaxerr2 pl_orbsmaxlim ... st_sperr st_splim st_lum \\\n", "count 1534.000000 2501.000000 ... 0.0 1335.0 584.000000 \n", "mean -0.429475 -0.000400 ... NaN 0.0 -0.136529 \n", "std 5.996049 0.019996 ... NaN 0.0 1.135486 \n", "min -200.000000 -1.000000 ... NaN 0.0 -3.480000 \n", "25% -0.040000 0.000000 ... NaN 0.0 -0.680000 \n", "50% -0.003100 0.000000 ... NaN 0.0 -0.024500 \n", "75% -0.000808 0.000000 ... NaN 0.0 0.479000 \n", "max 0.000000 0.000000 ... NaN 0.0 3.015000 \n", "\n", " st_lumerr1 st_lumerr2 st_lumlim st_age st_ageerr1 \\\n", "count 506.000000 493.000000 1401.0 2004.000000 1865.000000 \n", "mean 0.067524 -0.075288 0.0 4.161321 2.991969 \n", "std 0.090381 0.128068 0.0 2.162373 2.117259 \n", "min 0.000000 -1.431000 0.0 0.001000 0.001000 \n", "25% 0.027000 -0.077000 0.0 2.950000 1.140000 \n", "50% 0.049000 -0.050000 0.0 4.070000 2.500000 \n", "75% 0.079000 -0.028000 0.0 4.790000 4.890000 \n", "max 0.845000 0.000000 0.0 23.000000 26.000000 \n", "\n", " st_ageerr2 st_agelim \n", "count 1865.000000 904.0 \n", "mean -1.947577 0.0 \n", "std 1.087434 0.0 \n", "min -8.000000 0.0 \n", "25% -2.710000 0.0 \n", "50% -2.020000 0.0 \n", "75% -1.030000 0.0 \n", "max -0.001000 0.0 \n", "\n", "[8 rows x 79 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# if you are used to R at all, this is sort of like \"summary\" function, but basically giving some \n", "# summary statistics for the numerical data in our dataset\n", "planets.describe()\n", "# note that while things like the statistics for the orbital period are interesting\n", "# the \"mean\" of the pl_controvflag which is a flag if this planet is controversal or not\n", "# is essentially meaningless" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# skip\n", "# we can also search for subsets easily\n", "# we can look for only circular orbits\n", "# -> look for eccentricity == 0\n", "#planets.loc[planets[\"pl_orbeccen\"] == 0.0] " ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# skip\n", "# or very eccentric orbits\n", "#planets.loc[planets[\"pl_orbeccen\"] >= 0.9] " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0, 0.95)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# we can take min & maxes like with numpy arrays:\n", "# min and max of eccentricity\n", "planets['pl_orbeccen'].min(), planets['pl_orbeccen'].max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are things like \"groupby\" and things that we aren't likely to get into right now, but will come across naturally later." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZCVfM36LQhu1No6mtC6+s3ouDEetJwQQLIUTkuiwXFPPXuKuhnd5BxUtv4LW1+3C0I1xr7IbsWcYECDtvtuz5y4UU4u822ffnyl1N3uXtWU7uDwTjhwxTLKz99nCze7Wv/fy37G/BCQN6Ge7XilkxReDtso05mQce9tYYJwhbuDXo+Cr+sxduxeyFWx0TF+N+/s4c30/MiogHX8kFXw19BMCehAW01Tn0Mf9s8t0eb27IP8mbLHgqBFyvfCVqjZmA3a6e4evn39DSiRdW7sGBZv3OFjL7ZVKKfz7uW7TNVnqZLwDDRImwOf67Dx/Df/xzA7bXW59bzBZR6+evRzGhDV9j/h7g1dkdbY+hrqnNo9yYsCFE9F5Oix7k5ZAdegRG/C1hUFJ+ev5exuPdrlgPvLMNl/x+scu5MGFFQIRubp8gu5VSi7/RfWJXUP28QF7kzWEtxjds3Hth9vzdau+J3EpeeqJfVFfPMKijhXsrbF4VEy4iObePxNJjSfyJ6Foi2kpE1UQ00yTdl4hIENEk50y0jozlLPPFt0oUe7EUQxiuuRsIRGN6BydnEPA15k9EpQAeBXAdgIkAbiKiiTrp+gL4MYDldo1w+3YIf10M/xkGgah5tQURsjLSe9C/oVlvvJCaKdN8/hcAqBZC1AghugDMBTBNJ91/A7gXgO3ZxbLPNV8lsls2YfLEEgmB3725RXcSt5DVKyYA2OnNEqZ6mI1bD36/p3QeAaBW871O2ZaCiM4BMEoIMb8QI7JPUPeEi5vfwTfau+OOHm/FrkY8vrQGP3tpnaPHZRi3EYieg+J0e+MP5lQ5diwr4q93vVJnREQlAB4A8NO8ByK6hYiqiMi5M9AaZVDQYernn1DOsSuWSG0Ls0fFhIgQNvjqaY6T9TFbuxZuOujYsa2Ifx2AUZrvIwHs03zvC+B0AEuIaBeAiwDM02v0FUI8IYSYJIQwbRCes2w3Tv7VAjS1dVkwLz9hEEe1oUz9q3dK+SrWVx7/BLc868pzl4kodupWmBt8nTyrjPn8XdQuKxO7rQQwnojGAtgLYDqAr6o7hRDNAAar34loCYCfCSEKVhkhgHjC5KxtFkgItD9FSuALmPxpxc5Gp81hGMv827kjURo21z8PMmtPXvEXQsSIaAaAhQBKATwphNhERLMAVAkh5rllnFNPve54In+igGBWdVzzqqJVXxmXuOmCE/02wXH0myfTW6c/vgwJIfDWbZcWdnyfPX8IIRYAWJC17S6DtJcVa5TqHNju1WOwfXkIPd4wtWOEjVAMKmTsYeAgbd5/1Fs7bCDlCF+VhKYS1Rw6hq889glaO2O2jxOmyqiO4s2Y8zs8pxdooviCxPee88jUz18aVuxqxNKtDZH0edW3IfVv1e7cpSgjFk5lGN/xQqjdqtdSin++c9156Jit432y43DhxkiGbr9bdr8Yxlcylpx1sqtn1CZ2S2G3V49B+rkra/V3hAx2/BkmfLhVr6UUfzWunWCPNgcO7TAyEeWOB+tqj2DGc6tdzUPAvdl6pRT/9z6tB6DvybP45RaA29WPy5xhcjlwtAOHjyUHomrrSLH1Uft7N9dAkFL8VaLrUxhjKsQs0lIQpft24aaDvLSny0SqwVfF/opd4a12rOsBIKIXafoTy/w2wRe8uNxuTokht/jrbHvone2e2yET+r19PDeDkZDX1+7FA4u2obnNucVErHCotdPT/BhnkFv8dURt68EW7w2RCLPGn7BOmsVY4/W1+/DQu9sdXUmKMUZbFzNqXpHemDbi4fv0Dv7BLm02+vNrczkxwJPfPt9vEyKFZ65WNGP+7qYPImanyL1yGD+IQr3zCzcXwJFa/M1mdY4a6e6vXCiMXHTGwjNrrhXmr9+HLfuPZjhb2hCQEzV0Q10zYspsxJHs7cOkuXveJgAGN5bLzwN+obAHP5/DzW1z12L++n35ExbItoMt+MIjH2L2wq1KP/9I9vaxV4vqmtpdssR/9GbzzEnjkS2MPtzgHi0K8cj3HM4/JqKhJdl7asPeZrjp2Ukt/hz2yYXDPgzjL2oN3H6wNbUtY4SvQRX9cPshXDp7MV5bszdn3+7DxpNVRjLsw0KXRr3+lRXJDloThlWm9nEpMYy3EAj7mzts/ebTA8mFXdbXNefs+/zsJbq/EQJo64rbts8KUos/k0Z9+peXJi/ZrZeP00nDYQfGH2oaWvMnCgmqU1pW6k590/q86txBbiC1+LPjnyYV89fZx+XE+I3dNTaCDhFQVpqWzyC6XXKLPwc0Ulhx6tnxZxj3UVWpR6m+fOqFq8fMfMNFiwpDbvFn7ZcCDicxTCYEoCTg1YLFPyCY3Wf8hiQXfD2igbZrr5P+kVd3j9Tizyt5peF+/vLDL0jRIJ8sBUW1pBZ/1v402bqSMZzcpJwefGcbFm+td8cohokqRIF/2Est/uz5pym0wfeJ92vw0fZDzhvEMBqiWFUDrv2yi7/fFshEYbcaITivoQwTFAjZzpZzjwKvHqSSiz/Llkr6RsstE/NpnoPunzCMnBjVraDIltSLufD0DmlyYv6WUiUpthjVo5aWEEqD3r+NYYogTJokuefvtwXyUKgDnwz7OFiQfE3yw2UUeoiQNZ+/f7YUitTiH2f1T0EgrK87gu89U5Wzz9QbIedeQ+MJga54AtX10V5H2YgA1n/GJtq6ZDSFd7HVzatxIlKLP8f80xABh1o7sctkPnA978MNQWpu5wXCmWhD4K6ersLan4ZgPKLQzQbfOct2o7Yp/wIUTLSJSlXVnicZfA4KUjf4fnbcIL9NkAYiynuHGe0utJGqozuOO1/biL49s2+TIN7qDOMc2TH/IGLJ8yeia4loKxFVE9FMnf13ENFmIlpPRO8S0WgnjBvQuxy9y0udOFQosHqvbdzbjHve/BRNx7pAVLhXpt7cLZ2xAo/AAMmH6HPL94SqpwhjTLHXWZp+/kRUCuBRANcBmAjgJiKamJVsDYBJQogzAbwM4F6nDAz4w9Uxkp6GJuwD47jPpwda8NjSHWjpiCV7+xR4Mxn9Lugej9c8+M52/OrVDViw4YDfpjBFYiTsQRxPY8XzvwBAtRCiRgjRBWAugGnaBEKIxUIINTC8DMBIZ81kLER9NJO/ifRviHiWSZ853JpckLu1M7wN5VF7q+mKJfw2oWisiP8IALWa73XKNiO+C+BNvR1EdAsRVRFRbn9FxhQ7vQuMGqWcs4UxI1sG1e9GXQOZ4LBqdxMA4JHF1T5bUjxWxF/vjtV9zBPR1wFMAjBbb78Q4gkhxCQhxCQrxkXNmzAj6fnri8fnxg/Gxv+6BmeM6J/cINK/AbjXlFfkffUPifZ/Z/JYv03wjSMG3ZydvLRqdf14x2EHj5qLFfGvAzBK830kgH3ZiYjoKgC/BnCjEKLTGfOAYy6tXB80sieS0n7uUVqCyp49UlMvqGEeUqadLVT7+aHB6FFRJnUPcVcxqhO7DgdvDWMrV3ElgPFENJaIygFMBzBPm4CIzgHwOJLCz5PHu4TlsI/q+Sv/Oy3iQWzc8hN+iIYJ/YupHfiY73p/sL3BSYMKJq/4CyFiAGYAWAhgC4AXhRCbiGgWEd2oJJsNoBLAS0S0lojmGRyOKZASIssx41SMOdUXuTD14YZiZwnLI/O604fnbOM7xTrb61v9NgGAxUFeQogFABZkbbtL8/kqh+1SjuvGUYNJaYmNBt+U5+9OE2NYRIyxT3NbN045vi+2zLoWn7nrrdT2qNRV7XnaPWdt+q5YAuU99H1vr9o6oxu8CxilJWR7ODk3+MpBmN6gzpr1Nm5+egV6ZQ2+jErnjGLOUnsf3Prc6uKNKRIW/4BQQmQ51q69yaiIWT15kFdh5JRbqvcVYe+RdjQe6/LcJif5qNrdXihGCCFQ29iG5jb/xktYqUtWHvaLNh90wJriYPEPCIWFfZKhnzB5njJjdH3S/fyBL/35Y9zz5havTPIML+4wIYDP3bsYT32804Pc8jPyuN620sv2csTiHxCywz5mpO4xKtLzL+xnKWLxBI51xnhdBoViroXseDH9ekkJoYSAWNy/QtQ6UtefcbzN38oFi39A6JHl+Zu+BajTOygNvn7ddMtqGnHafy7E6j1NPlkgHwIipxm+/mgHxsx8A/PX5wyfCQxePdR6lJagO+Hf1AoZi7nYXMNXtgc/i39A+Mr5o2C1n01mV8/CA/RFz04ona/jD9pyFCL3wb31YHJltLkrahFUvLrS5aUl6I4F876SrT5ILf6yPSn95LrTj88QjdrGdsO0mYO8nC9Hq8fLtiPqqNcvW/zDcJ//+Pk1WLW70fV8epQSYn56/kbbA3gNpRZ/JhOtZrxvMkowPatn5nQPdil+LVIodhR5oBBhvuqaZ2a4ghcCePNnx2LyuMHuZ2RAMW/Ddp0mt2HxDxDaEE5ZqfGl0/YuIU3Qf9zQSkw9I3N05pX3LcFD72x31tAcAq5qFrlt7loAuQ9b7bdkxSbD/dk8/O52XPGHJU6Y5zpePLx+ctV4XHOavYZWrzF+O5Dr9YDFP0AM6lOe+qxO4qZHKtyi9vZJbc+9+Wob29HerT95XrH3qmw3u9scPqY/n6F2xDUgdMI+xuXU3N6NA0c7HLLQbaLxkA8LUou/bA0kfkJEGDWwNyYMqwSQ7P1jhHb++JwOopqvsXgCXfGEa0tlRi3so2r4Y0t36O5Xu3oaFYde47xAcCQ1KtdZD5PqKC1Siz+TS7fSx7mHWdgnq6XVyLOMJQSmTByGcUMrDQ5UsJkZvw9gvSgItbiyR8CeMKAXAKCnMpdLjudvdkwRnFlU3baytrEttSqaXxi9pN141oi8aWoOWZv22Sunl8U/YHTHkz0dyiy4Gtlhn2wqykrxl29OwvVKO8Dh1k5842/L8ecl+p5roQRFvIrGoKAvHZ9soBzQu9y8wdfmdr/56oUnZnx3+zrf+MiHeND19ilzjITZaJI2La+s3uu0OUXB4h8w1NGyPUotxPyVf+r3fP6EANDaGUOHQRuAXThsp6CZYE+I3EFeZsUkcxneftUEz/OU1Y+Q1S4zpBb/iLUXmvLbNzYjnhCpsE9piVlvH+1KXmR5Td/BlT3x6o8m4/YpEzKOUyhR6+dvVF5asRewJxRC4qB/9vm6baYMcrCuttn1PMzG8DiJ1OLPpHl+RS1qG9vQtyK5BEO5ieffr6IMowf1Rgn5qxvaXkdRIJ+zknooG+w3KqeIFF9ezBrLvaLBoM1Be+2KdZruX7StqN9bhcU/QLR3xzH9/ORyymYxxukXnIilP78cvcuTD4pCu1w69eblzpIywSElDEK/TM3EQgghb5uJMP3qfHYSlEVFD/2ecUG8x6UWfxle82ShtITQHU/YX6BF2+DrUIHeMqcKc5btzpsuatfP6Hy1smAmYHbfCGTD7XEdMtxPXzznBN3tQblGWqQWfybN+v+8GmeOHGDbw8hewpco+RBpbutGLJ5AfUsHHn53O2oactcVNapsB4926qbP+X1qmglbJgeWfOInYF/AZBA8q7g+c7fOpHhec+LA5Bz+owb2ytheojEsKG2VLP4BodD7Kdngm/nrj6oP4axZb2P93mYcbO7EfYu2YUeDtT7IKXssGBSQOuA62Z6+nYndZIhzG5Frtvuev6zhFTmtMofFP6BYrWbtXXEs2HAA1fVpT131UhIJkXow6A0bMPNk7bzi++2teUXerrQGrr+6ItTJQ3IH2wn4H+e2ituefzJk5m4e+VAfPtm3v992FYLU4i+EwOhB9pZKCzt2bzJ1vdjVe5pSuqPOC/Tf8zenKqzd41qp50F5/XWKfGseq4/abO91aN+eAIARx/VCNjJ7/tm4fb3d7PU661+bcdfrGwv+fSEP6ITPK9xJLf4AcEL/3AoRRbI9basVrSOWHLClzt+TmukTwLq65nRcXqda5Zt2ID/Gx44SuQ2+WfvtNuJLQvbSjW4v5ai3EI5TrK1tQo3N0KcR371krKV0cWVB+jtfK/yhUww9fMmVsU2h1Uqtj73K0l3USrWNU+oHm5XKSkWPUj//PYfb8qYRQt97TYUS9H4Decsve21m9z1/90JgbV1xDLfgaA7oUwYAmHrmcMM0FWXWJkqMJwTueHEtVu7yZ5lT6cVfQOC80cdh1W4r2rK0AAAPKklEQVReB1aL1YEkYwb1RkIA548dmPLytdNBq9tKbFYqO/Xc6NBNx7pQQoT+vcts5S0j2utxSdZiI+mwT+b3dILkn5U7G3O8Rr35/2UhW+zdnorCzRDYW7ddilg8/wph/SrKsP7uq1FZXrx0xhPC/R5SJkgd9hFIXnCzuevd5qQhfXzLW0vai7ZXFhVlpTj1+L7oV1GG7rjAvHX70NaVnrtHvd/1jpqvF0o+djeae8P/588f4zdFxFllQvvwzL1E1roBvre1Xmer/42cRmR7/p5073KxLMxmytXSr6IMJQ5oUtznOJ/U4g/4O5/5HVMm4F8zLvEpd2DFr6803GfnvlGT7j2SnDPkoXfTMyOq66HaF5j8Btzz5qfJYxtcwbLSEnTH/FuP1SvGD6vEP753Ic4c0d80dFFuID52Ls3CTQfw/+dvLsDK/GS3O2WL16z5m3Hx795Nfe/ojqO6vhUtHd3O5O/IUdL43eAaj7P458Uvz2dI357o09O/yNjQvhWpuXwKnRufiHIeFEMqe6Y+x+LGYR+z13g7a2gbXb+yHoQuC6/aQUDrCWaLe7+KMkweNxjH9SnXDV2o4YYynfma7DqHq/c0WRp9nc1X/7IM33umytZvssXz0wMt2N+cXnVsR0Mrrrp/ac76BgUjnO088JcPanD2rLfR1hVz7Jh2sOP5p3TAQaQX/9uuHI8fXzHel7xlaLAsNmttU+LoQb0x7ewTMOOKcan96qu73XysxHfV1caMjl1WWpJanyDoaKMAecsyK0HfijKUlRJ+PXViTlK7PVxKiXLDMRYgAhoNlqHU2qIln3ipi9c4dY2FwyGwPUpYsrcD8ftCsPPm4YYESd3gKwTw2azGM7tMPWM43tiwv7D8kfaKvYrPXXHqULz3aW7sNz1Ns73jqUsHpr4jsw0lljB5pSgy5t+jlNLH16FvRZnpcpRBwmqDuV5plPcowfbfXm+QXmf+fxNKSwq7V089vh8aWuytkhXLE7YoU8JYXQ6F9uw0+J4z621MGNYXL/zgYsM0P736FHztwtGO2FYIZnUjGzd6Ofnq+d90wSjX8+hZVvgpql0i3dSnJT+7LEMAH/v6eRn71Yue07PCYgXXW8lLK1RxNeZv07ewknuPEv1lC1We/c4FePLb59vKV1a0p2hWT++8YSKunjjM8nHtev67D7dBiOTC73a484aJ+ONN55jbkvX9aJ48yh33/K2XRVNbN5bvbDRNM7BPOSae0K94wwrEzhuaG9EHX8X/9bX7XM/DTCPHDa3EGSP6G+7/wlnJGfxuufQkp81KMWZwH5w/ZmDqe3mPEvTVtDOoI5xL8oRQjNATdW27Yncq5p/722IHeaXfMMLh3Zuh9czMzvYbF43GeaMHmqTQObaNtOvqjgBIdqN1mmyHo7HNPA/V83dM/PVWQZOQ+qMd+RPBpvgXaowJlsSfiK4loq1EVE1EM3X29ySiF5T9y4lojJXjarsc6lN8qMVsMJIQAi/90Pi1UG3s/fk1p+LJb08q2IbzRh9nuj87fv7W7ZfiqZuTHvFT3z4fj339PPTvVXhfeLXS6nUXTcX87fbzt6D+qZi//PW1aDJi/g6esN0aoIZYyiysKVssJw2uxGWnDMnYNveWi1KfVc+/06mwD4JxL7V0WmtAjgtheVS0L2EfIioF8CiA6wBMBHATEWW3TH0XQJMQYhyABwD83mlDC8W0rzqS/eCf+MZ5xokUimkUeukHF+Mpk/BGto0jBvTC5acMBQAMquyJa08/Pvc3FvPODvsQUcYIX9Ur07u38pVdPlo6kpWg0sceU15RYtHzt0sy7GP9iGePGgAAjgxCyrFF+XuWksfEE/rhZ1efkpHmopMGpT6Xpzx/Z9rLgjLPkdU3nfnr9ltu9PWrwfcCANVCiBoAIKK5AKYB0HYmngbgbuXzywAeISISbq/uYAFzz9/6cS4cm35Vf2j62Xhu+Z68MUWVkhIyHRSi2vGbqZ/JeyyjNgDD9DpptQ2+Zr19DhksWZfMP78BajfOgX3K86YNOuaDvAonYXMmy/u+chZuvXycq6Omp3xmqKV0Tod9AFgu3I9mXmFpxK4b5GsIV3ngHW+WazTCyrvhCAC1mu91yjbdNEKIGIBmAIOQh59cOd613h59lInMzJY7VLui9TBZD1dF631NO3sERgywN+Gc2XmqjdKq12aGWqHmLNuN/c0WFnomwtJtDdmbUsx8ZYOyLdO+tbVHcMPDHxoeVu/2PtzaiSn3L8X/LNgCAHjq5vNx3enHp2wONZT75Z43P8WU+5cW1dulvqUDw/pVWE7fu7wHTjdox7rztY2Ycv/S1GA/q3THE5hy/1L8afGOnH3a22ZA1gOntISw43+ux4+vLL6r9hGlfcGqWowY0AujB7kzOv9PS6ox5f6lhg81s3pTKHbuAatY8fytdAK01FGQiG4BcAsADB4xFrdPmYDbp0zAHS+sxStr9uYcQDvA6rVbJ+Ou1zfitBP648KxA9G7vBQdsQR+/PwaXaPf/8Xl+MsHO3H7lPGobWzDyl1NeOGWi7BkWwP+vGQHbp48Bt+ZnJxH5fMThuJHl52MqWcOx9ubDuKkIX10vdU/3nQOjlNu8NunTMD89fvRFU/gjikTcP+ibZhx+TiMGtgLb248gH4VZfi3c0ek+hJffNIg3Hr5yfjahaPxzMe7cHz/ilRj831fPgtzlu3GuSeatw0AwJfOG4m/fliDEQN6pXrTmPGti0djibL60OWnDMHYwX0wvH8vXD1xGN7efBD9e5Vh8rhBqfNSGT+0EmeO7I/1dc0Y1KccpxzfF+3dcVx80iAc64zpVqweJSUYP6wydaNefsrQVPgq7JSXlmDqGcOxandTap3l4/v1xPhhlUW9CTx98wVo7XBmENLwARUYP6xSdzBZPsYPq8TQfj0x9YzhGDc0ve7AyUMqcdMFo1Db2I67bzwt53dOTc1SUkK44czhuOa03BCo1wyuTF5XIOlcqjPmqpx2Qj9s2nc09X36+aPwwfZDth+6KpedMgS3XzUBa/Y0Yd66fbA/hE8fyvf6TkQXA7hbCHGN8v2XACCE+J0mzUIlzSdE1APAAQBDzMI+kyZNElVV9kYUMgzDRB0iWiWEKLwHioKV9/GVAMYT0VgiKgcwHcC8rDTzAHxL+fwlAO/JEO9nGIZh9Mkb9hFCxIhoBoCFAEoBPCmE2EREswBUCSHmAfgbgDlEVA2gEckHBMMwDCMplvqDCSEWAFiQte0uzecOAF921jSGYRjGLSLQDYNhGIbJhsWfYRgmgrD4MwzDRBAWf4ZhmAjC4s8wDBNB8g7yci1johYAW33J3B6DARzy24g8BMFGgO10miDYGQQbgWDZ2UcIMSRvyjz4Od3iVidGqbkNEVXJbmcQbATYTqcJgp1BsBEInJ1jnDgWh30YhmEiCIs/wzBMBPFT/J/wMW87BMHOINgIsJ1OEwQ7g2AjEEE7fWvwZRiGYfyDwz4MwzARxBfxz7cgvMe27CKiDUS0loiqlG0DiWgREW1X/h6nbCci+qNi93oiOtdFu54konoi2qjZZtsuIvqWkn47EX1LLy8X7LybiPYqZbqWiK7X7PulYudWIrpGs921e4KIRhHRYiLaQkSbiOgnynapytPETmnKk4gqiGgFEa1TbPwvZftYIlqulMsLyvTvIKKeyvdqZf+YfLa7bOfTRLRTU5ZnK9t9q0NKHqVEtIaI5ivf3S9PIYSn/5CcFnoHgJMAlANYB2Ci13Zo7NkFYHDWtnsBzFQ+zwTwe+Xz9QDeRHLlsosALHfRrksBnAtgY6F2ARgIoEb5e5zy+TgP7LwbwM900k5UrndPAGOV+6DU7XsCwHAA5yqf+wLYptgiVXma2ClNeSplUql8LgOwXCmjFwFMV7Y/BuD/Kp9/BOAx5fN0AC+Y2e5gWRrZ+TSAL+mk960OKfncAeA5APOV766Xpx+ef2pBeCFEFwB1QXiZmAbgGeXzMwC+qNn+rEiyDMAAIhruhgFCiPeRXBuhGLuuAbBICNEohGgCsAjAtR7YacQ0AHOFEJ1CiJ0AqpG8H1y9J4QQ+4UQq5XPLQC2ILnutFTlaWKnEZ6Xp1ImrcrXMuWfAHAFgJeV7dllqZbxywCuJCIysd0RTOw0wrc6REQjAUwF8FflO8GD8vRD/K0sCO8lAsDbRLSKkmsMA8AwIcR+IFkhAagL0fptu127/LR3hvL6/KQaTjGxxzM7ldfkc5D0BKUtzyw7AYnKUwlRrAVQj6QY7gBwRAihLjaszS9li7K/GcAgt23Us1MIoZblb5WyfICIembbmWWPF9f8QQC/AKCuCD8IHpSnH+JvabF3D5kshDgXwHUAbiWiS03Syma7ipFdftn7ZwAnAzgbwH4A9ynbfbWTiCoB/BPAbUKIo2ZJDezxy06pylMIERdCnA1gJJLe5WdM8vOtLLPtJKLTAfwSwKkAzkcylPMfftpJRDcAqBdCrNJuNsnTMTv9EP86AKM030cC2OeDHQAAIcQ+5W89gFeRvJkPquEc5W+9ktxv2+3a5Yu9QoiDSsVLAPgL0q+fvtlJRGVICuo/hBCvKJulK089O2UsT8WuIwCWIBkjH0BE6nQx2vxStij7+yMZJvTs3tTYea0SWhNCiE4AT8H/spwM4EYi2oVkeO4KJN8E3C9Ppxsu8v1Dcj6hGiQbJdTGqNO8tkOxpQ+AvprPHyMZz5uNzIbAe5XPU5HZKLTCZfvGILMh1ZZdSHo2O5FsqDpO+TzQAzuHaz7fjmQsEgBOQ2ajVA2SjZOu3hNKuTwL4MGs7VKVp4md0pQngCEABiifewH4AMANAF5CZgPlj5TPtyKzgfJFM9sdLEsjO4dryvpBAPfIUIeUvC5DusHX9fJ0/AQsnuT1SPZk2AHg137YoNhxklJg6wBsUm1BMob2LoDtyt+BmhvmUcXuDQAmuWjb80i+4ncj+VT/biF2AfgOko0/1QBu9sjOOYod6wHMQ6Z4/VqxcyuA67y4JwBcguQr8HoAa5V/18tWniZ2SlOeAM4EsEaxZSOAuzR1aYVSLi8B6Klsr1C+Vyv7T8pnu8t2vqeU5UYAf0e6R5BvdUiTz2VIi7/r5ckjfBmGYSIIj/BlGIaJICz+DMMwEYTFn2EYJoKw+DMMw0QQFn+GYZgIwuLPMAwTQVj8GYZhIgiLP8MwTAT5X7qqVims5vybAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# pandas also provides a matplotlib-like interface\n", "# to make quick plots to look at our data\n", "planets[\"pl_orbeccen\"].plot() # easy plots with pandas dataframes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note this ils like doing a `matplotlib` style plot, but now our plot is associated with our data.\n", "\n", "So we can see there are a lot of both zero and less very eccentric planets. \n", "\n", "Note also that there are a lot of empty spots - this indicates where there are \"NaN\"s - or non-entries.\n", "\n", "So, maybe this isn't what we want to know - we really want to know about the eccentricity *distribution* - so let's plot that:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "planets[\"pl_orbeccen\"].hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can add labels and things like this like we'd do with `matplotlib` type plots, but the way we do it is a little different:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Number of observed planets with this eccentricity')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "myPlot = planets[\"pl_orbeccen\"].hist()\n", "myPlot.set_xlabel('Eccentricity')\n", "myPlot.set_ylabel('Number of observed planets with this eccentricity')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are getting some where - it seems like there are many near-circular orbits in the Kepler dataset!\n", "\n", "We can make the same sort of plot using `matplotlib` as well:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# first, create an axis object\n", "fig, ax = plt.subplots(1, 1, figsize = (10, 10))\n", "\n", "# set this histogram to be on this ax object\n", "planets[\"pl_orbeccen\"].hist(ax=ax)\n", "\n", "# add labels with ax:\n", "ax.set_xlabel('Eccentricity')\n", "ax.set_ylabel('Number of Kepler Planets with an eccentricty')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Styles\n", "Now, lets play with something called the \"style\" of the plot. If you've used R, or come across it later in life you might/may have used the \"ggplot\" package. We can make plots in this style with Python too:" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with plt.style.context(\"ggplot\"):\n", " # first, create an axis object\n", " fig, ax = plt.subplots(1, 1, figsize = (10, 10))\n", "\n", " # set this histogram to be on this ax object\n", " planets[\"pl_orbeccen\"].hist(ax=ax)\n", "\n", " # add labels with ax:\n", " ax.set_xlabel('Eccentricity')\n", " ax.set_ylabel('Number of Kepler Planets with an eccentricty')\n", "\n", " plt.show() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What plot styles are available to us?" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['seaborn-dark',\n", " 'seaborn-darkgrid',\n", " 'seaborn-ticks',\n", " 'fivethirtyeight',\n", " 'seaborn-whitegrid',\n", " 'classic',\n", " '_classic_test',\n", " 'fast',\n", " 'seaborn-talk',\n", " 'seaborn-dark-palette',\n", " 'seaborn-bright',\n", " 'seaborn-pastel',\n", " 'grayscale',\n", " 'seaborn-notebook',\n", " 'ggplot',\n", " 'seaborn-colorblind',\n", " 'seaborn-muted',\n", " 'seaborn',\n", " 'Solarize_Light2',\n", " 'seaborn-paper',\n", " 'bmh',\n", " 'tableau-colorblind10',\n", " 'seaborn-white',\n", " 'dark_background',\n", " 'seaborn-poster',\n", " 'seaborn-deep']" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt.style.available" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But what if we want to see how our plot would look with each of these styles? We could just make a bunch of plots OR we can play with this interactively with ipywidgets:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "import ipywidgets" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "08dfc9f3c6e74d70a1d07e392393165c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='style', options=('seaborn-dark', 'seaborn-darkgrid', 'seaborn-tick…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# lets tell jupyter ipywidgets that we want to \n", "# mess around with the style of the plot\n", "@ipywidgets.interact(style = plt.style.available)\n", "def make_plot(style):\n", " with plt.style.context(style):\n", " # first, create an axis object\n", " fig, ax = plt.subplots(1, 1, figsize = (10, 10))\n", "\n", " # set this histogram to be on this ax object\n", " planets[\"pl_orbeccen\"].hist(ax=ax)\n", "\n", " # add labels with ax:\n", " ax.set_xlabel('Eccentricity')\n", " ax.set_ylabel('Number of Kepler Planets with an eccentricty')\n", "\n", " plt.show() \n", " \n", "# so now you can see that we get a little dropdown menu that lists\n", "# all the different styles!\n", "# **play with this a bit!!**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, but what did we just do? We made something in Jupyter interactive. we'll have a lot of opportunities to mess around with widgets in the next class. If you want, you can read more on the docs: https://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html\n", "\n", "Basically what ipywidgets.interact does is looks for a function with inputs and makes a little interactive option for those inputs, so like we did with the \"make_plot\" function above we can do for other things like change the value of a print statement:\n", "```\n", "@ipywidgets.interact(x=10)\n", "def f(x):\n", " print(\"my value = \" + str(x))\n", "```\n", "\n", "Note this is a little different to the format in the docs and you can use what you'd like the \"@\" symbol is a \"decorator\" and essentially its a way to sort of \"extend\" the interact function without modifying it to much.\n", "\n", "At any rate, the take away is that you can call it like this, or how they do it in the docs, its up to you!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Another example: changing the histogram binning\n", "We can actually run multiple widgets to do many interactive things at the same time, for example, plot style and histogram binning:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b421627e219a4ba8b38a42e565124fdd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "interactive(children=(Dropdown(description='style', options=('seaborn-dark', 'seaborn-darkgrid', 'seaborn-tick…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "@ipywidgets.interact(style = plt.style.available, number_of_bins = range(1,20,1))\n", "def make_plot(style, number_of_bins):\n", " with plt.style.context(style):\n", " # first, create an axis object\n", " fig, ax = plt.subplots(1, 1, figsize = (10, 10))\n", "\n", " # set this histogram to be on this ax object\n", " planets[\"pl_orbeccen\"].hist(ax=ax, bins=number_of_bins)\n", "\n", " # add labels with ax:\n", " ax.set_xlabel('Eccentricity')\n", " ax.set_ylabel('Number of Kepler Planets with an eccentricty')\n", "\n", " plt.show() \n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "Pick another variable besides eccentricity and repeat this exercise. Check out the header of the data file for lists of what the other parameters are.\n", "\n", "Bonus: Pick another plotting variable besides style (like color of bars) to change. You might want to check up on the parameters avaiable under the pandas python plot: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.hist.html and general matplotlib plots: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.hist.html\n", "\n", "Bonus: make the variable another ipywidget input\n", "\n", "Bonus: instead of a histogram, plot one variable vs. another\n", "\n", "Bonus: make a 2 panel plot with the ability to change different things on different plots" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }