diff --git a/Intro to PanDas-checkpoint.ipynb b/Intro to PanDas-checkpoint.ipynb new file mode 100644 index 0000000..870edc9 --- /dev/null +++ b/Intro to PanDas-checkpoint.ipynb @@ -0,0 +1,844 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n", + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", + " 24 25]\n", + " \n", + "{'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4, 'F': 5, 'G': 6, 'H': 7, 'I': 8, 'J': 9, 'K': 10, 'L': 11, 'M': 12, 'N': 13, 'O': 14, 'P': 15, 'Q': 16, 'R': 17, 'S': 18, 'T': 19, 'U': 20, 'V': 21, 'W': 22, 'X': 23, 'Y': 24, 'Z': 25}\n", + "\n" + ] + } + ], + "source": [ + "a_ascii = ord('A')\n", + "z_ascii = ord('Z')\n", + "alphabets = [chr(i) for i in range(a_ascii, z_ascii+1)]\n", + "\n", + "print(alphabets)\n", + "\n", + "numbers = np.arange(26)\n", + "\n", + "print(numbers)\n", + "\n", + "print(type(alphabets), type(numbers))\n", + "\n", + "alpha_numbers = dict(zip(alphabets, numbers))\n", + "\n", + "print(alpha_numbers)\n", + "\n", + "print(type(alpha_numbers))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 A\n", + "1 B\n", + "2 C\n", + "3 D\n", + "4 E\n", + "5 F\n", + "6 G\n", + "7 H\n", + "8 I\n", + "9 J\n", + "10 K\n", + "11 L\n", + "12 M\n", + "13 N\n", + "14 O\n", + "15 P\n", + "16 Q\n", + "17 R\n", + "18 S\n", + "19 T\n", + "20 U\n", + "21 V\n", + "22 W\n", + "23 X\n", + "24 Y\n", + "25 Z\n", + "dtype: object\n" + ] + } + ], + "source": [ + "series1 = pd.Series(alphabets)\n", + "print(series1)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 0\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + "5 5\n", + "6 6\n", + "7 7\n", + "8 8\n", + "9 9\n", + "10 10\n", + "11 11\n", + "12 12\n", + "13 13\n", + "14 14\n", + "15 15\n", + "16 16\n", + "17 17\n", + "18 18\n", + "19 19\n", + "20 20\n", + "21 21\n", + "22 22\n", + "23 23\n", + "24 24\n", + "25 25\n", + "dtype: int32\n" + ] + } + ], + "source": [ + "series2 = pd.Series(numbers)\n", + "print(series2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "A 0\n", + "B 1\n", + "C 2\n", + "D 3\n", + "E 4\n", + "F 5\n", + "G 6\n", + "H 7\n", + "I 8\n", + "J 9\n", + "K 10\n", + "L 11\n", + "M 12\n", + "N 13\n", + "O 14\n", + "P 15\n", + "Q 16\n", + "R 17\n", + "S 18\n", + "T 19\n", + "U 20\n", + "V 21\n", + "W 22\n", + "X 23\n", + "Y 24\n", + "Z 25\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "series3 = pd.Series(alpha_numbers)\n", + "print(series3)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "A 0\n", + "B 1\n", + "C 2\n", + "D 3\n", + "E 4\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "series3.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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AlcoholMalic_acidAshAlcalinity_of_ashMagnesiumTotal phenolsFlavanoidsNonflavonoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
01NaN1.782.1411.21002.652.760.261.284.3800001.053.401050
11NaN2.362.6718.61012.803.240.302.815.6800001.033.171185
21NaN1.952.5016.81133.853.490.242.187.8000000.863.451480
3113.242.592.8721.01182.802.690.391.824.3200001.042.93735
4114.21.762.4515.21123.273.390.341.976.7500001.052.851450
5114.391.872.4514.6962.502.520.301.985.2500001.023.581290
6114.062.152.6117.61212.602.510.311.255.0500001.063.581295
7114.831.642.1714.0972.802.980.291.985.2000001.082.851045
81NaN1.352.2716.0982.983.150.221.857.2200001.013.551045
9114.12.162.3018.01052.953.320.222.385.7500001.253.171510
10114.121.482.3216.8952.202.430.261.575.0000001.172.821280
11113.751.732.4116.0892.602.760.291.815.6000001.152.901320
12114.751.732.3911.4913.103.690.432.815.4000001.252.731150
13114.381.872.3812.01023.303.640.292.967.5000001.203.001547
14113.631.812.7017.21122.852.910.301.467.3000001.282.881310
15114.31.922.7220.01202.803.140.331.976.2000001.072.651280
16113.831.572.6220.01152.953.400.401.726.6000001.132.571130
17114.191.592.4816.51083.303.930.321.868.7000001.232.821680
18113.643.102.5615.21162.703.030.171.665.1000000.963.36845
19114.061.632.2816.01263.003.170.242.105.6500001.093.71780
20112.933.802.6518.61022.412.410.251.984.5000001.033.52770
21113.711.862.3616.61012.612.880.271.693.8000001.114.001035
22112.851.602.5217.8952.482.370.261.463.9300001.093.631015
23113.51.812.6120.0962.532.610.281.663.5200001.123.82845
24113.052.053.2225.01242.632.680.471.923.5800001.133.20830
25113.391.772.6216.1932.852.940.341.454.8000000.923.221195
26113.31.722.1417.0942.402.190.271.353.9500001.022.771285
27113.871.902.8019.41072.952.970.371.764.5000001.253.40915
28114.021.682.2116.0962.652.330.261.984.7000001.043.591035
29113.731.502.7022.51013.003.250.292.385.7000001.192.711285
.............................................
147313.323.242.3821.5921.930.760.451.258.4200000.551.62650
148313.083.902.3621.51131.411.390.341.149.4000000.571.33550
149313.53.122.6224.01231.401.570.221.258.6000000.591.30500
150312.792.672.4822.01121.481.360.241.2610.8000000.481.47480
151313.111.902.7525.51162.201.280.261.567.1000000.611.33425
152313.233.302.2818.5981.800.830.611.8710.5200000.561.51675
153312.581.292.1020.01031.480.580.531.407.6000000.581.55640
154313.175.192.3222.0931.740.630.611.557.9000000.601.48725
155313.844.122.3819.5891.800.830.481.569.0100000.571.64480
156312.453.032.6427.0971.900.580.631.147.5000000.671.73880
157314.341.682.7025.0982.801.310.532.7013.0000000.571.96660
158313.481.672.6422.5892.601.100.522.2911.7500000.571.78620
159312.363.832.3821.0882.300.920.501.047.6500000.561.58520
160313.693.262.5420.01071.830.560.500.805.8800000.961.82680
161312.853.272.5822.01061.650.600.600.965.5800000.872.11570
162312.963.452.3518.51061.390.700.400.945.2800000.681.75675
163313.782.762.3022.0901.350.680.411.039.5800000.701.68615
164313.734.362.2622.5881.280.470.521.156.6200000.781.75520
165313.453.702.6023.01111.700.920.431.4610.6800000.851.56695
166312.823.372.3019.5881.480.660.400.9710.2600000.721.75685
167313.582.582.6924.51051.550.840.391.548.6600000.741.80750
168313.44.602.8625.01121.980.960.271.118.5000000.671.92630
169312.23.032.3219.0961.250.490.400.735.5000000.661.83510
170312.772.392.2819.5861.390.510.480.649.8999990.571.63470
171314.162.512.4820.0911.680.700.441.249.7000000.621.71660
172313.715.652.4520.5951.680.610.521.067.7000000.641.74740
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AlcoholMalic_acidAshAlcalinity_of_ashMagnesiumTotal phenolsFlavanoidsNonflavonoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
58212.370.941.3610.6881.980.570.280.421.951.051.82520
99212.082.081.7017.5972.232.170.261.403.301.272.96710
65213.111.011.7015.0782.983.180.262.285.301.123.18502
75213.030.901.7116.0861.952.030.241.464.601.192.48392
68212.211.191.7516.81511.851.280.142.502.851.283.07718
109211.463.741.8219.51073.182.580.243.582.900.752.81562
102211.821.721.8819.5862.501.640.371.422.060.942.44415
100212.61.341.9018.5881.451.360.291.352.451.042.77562
61213.671.251.9218.0942.101.790.320.733.801.232.46630
66212.371.171.9219.6782.112.000.271.044.681.123.48510
74211.661.881.9216.0971.611.570.341.153.801.232.14428
107212.221.291.9419.0922.362.040.392.082.700.863.02312
77212.330.991.9514.81361.901.850.352.763.401.062.31750
103212.511.731.9820.5852.201.920.321.482.941.043.57672
117212.773.431.9816.0801.631.250.430.833.400.702.12372
96212.291.411.9816.0852.552.500.291.772.901.232.74428
115211.821.471.9920.8861.981.600.301.531.950.953.33495
1182123.432.0019.0872.001.640.371.871.280.933.05564
792120.922.0019.0862.422.260.301.432.501.383.12278
60212.641.362.0216.81002.021.410.530.625.750.981.59450
49113.051.732.0412.4922.723.270.172.917.201.122.911150
972NaN1.072.1018.5883.523.750.241.954.501.042.77660
153312.581.292.1020.01031.480.580.531.407.600.581.55640
37113.071.502.1015.5982.402.640.281.373.701.182.691020
43113.051.772.1017.01073.003.000.282.035.040.883.35885
46113.91.682.1216.01013.103.390.212.146.100.913.33985
105212.251.732.1219.0801.652.030.371.633.401.003.17510
40113.413.842.1218.8902.452.680.271.484.280.913.001035
122213.055.802.1321.5862.622.650.302.012.600.733.10380
01NaN1.782.1411.21002.652.760.261.284.381.053.401050
.............................................
156312.453.032.6427.0971.900.580.631.147.500.671.73880
136312.535.512.6425.0961.790.600.631.105.000.821.69515
158313.481.672.6422.5892.601.100.522.2911.750.571.78620
20112.933.802.6518.61022.412.410.251.984.501.033.52770
33113.511.802.6519.01102.352.530.291.544.201.102.871095
11NaN2.362.6718.61012.803.240.302.815.681.033.171185
70213.861.512.6725.0862.952.860.211.873.381.363.16410
56113.291.972.6816.81023.003.230.311.666.001.072.841270
52113.771.902.6817.11153.002.790.391.686.301.132.931375
167313.582.582.6924.51051.550.840.391.548.660.741.80750
29113.731.502.7022.51013.003.250.292.385.701.192.711285
139312.932.812.7021.0961.540.500.530.754.600.772.31600
14113.631.812.7017.21122.852.910.301.467.301.282.881310
157314.341.682.7025.0982.801.310.532.7013.000.571.96660
32113.761.532.7019.51322.952.740.501.355.401.253.001235
108211.611.352.7020.0942.742.920.292.492.650.963.26680
141313.523.172.7223.5971.550.520.500.554.350.892.06520
15114.31.922.7220.01202.803.140.331.976.201.072.651280
121212.424.432.7326.51022.202.130.431.712.080.923.12365
95211.812.122.7421.51341.600.990.141.562.500.952.26625
176314.134.102.7424.5962.050.760.561.359.200.611.60560
151313.111.902.7525.51162.201.280.261.567.100.611.33425
126211.792.132.7828.5922.132.240.581.763.000.972.44466
27113.871.902.8019.41072.952.970.371.764.501.253.40915
35113.281.642.8415.51102.602.680.341.364.601.092.78880
168313.44.602.8625.01121.980.960.271.118.500.671.92630
3113.242.592.8721.01182.802.690.391.824.321.042.93735
111211.762.682.9220.01031.752.030.601.053.801.232.50607
24113.052.053.2225.01242.632.680.471.923.581.133.20830
120211.562.053.2328.51193.185.080.471.876.000.933.69465
\n", + "

177 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " Alcohol Malic_acid Ash Alcalinity_of_ash Magnesium Total phenols \\\n", + "58 2 12.37 0.94 1.36 10.6 88 1.98 \n", + "99 2 12.08 2.08 1.70 17.5 97 2.23 \n", + "65 2 13.11 1.01 1.70 15.0 78 2.98 \n", + "75 2 13.03 0.90 1.71 16.0 86 1.95 \n", + "68 2 12.21 1.19 1.75 16.8 151 1.85 \n", + "109 2 11.46 3.74 1.82 19.5 107 3.18 \n", + "102 2 11.82 1.72 1.88 19.5 86 2.50 \n", + "100 2 12.6 1.34 1.90 18.5 88 1.45 \n", + "61 2 13.67 1.25 1.92 18.0 94 2.10 \n", + "66 2 12.37 1.17 1.92 19.6 78 2.11 \n", + "74 2 11.66 1.88 1.92 16.0 97 1.61 \n", + "107 2 12.22 1.29 1.94 19.0 92 2.36 \n", + "77 2 12.33 0.99 1.95 14.8 136 1.90 \n", + "103 2 12.51 1.73 1.98 20.5 85 2.20 \n", + "117 2 12.77 3.43 1.98 16.0 80 1.63 \n", + "96 2 12.29 1.41 1.98 16.0 85 2.55 \n", + "115 2 11.82 1.47 1.99 20.8 86 1.98 \n", + "118 2 12 3.43 2.00 19.0 87 2.00 \n", + "79 2 12 0.92 2.00 19.0 86 2.42 \n", + "60 2 12.64 1.36 2.02 16.8 100 2.02 \n", + "49 1 13.05 1.73 2.04 12.4 92 2.72 \n", + "97 2 NaN 1.07 2.10 18.5 88 3.52 \n", + "153 3 12.58 1.29 2.10 20.0 103 1.48 \n", + "37 1 13.07 1.50 2.10 15.5 98 2.40 \n", + "43 1 13.05 1.77 2.10 17.0 107 3.00 \n", + "46 1 13.9 1.68 2.12 16.0 101 3.10 \n", + "105 2 12.25 1.73 2.12 19.0 80 1.65 \n", + "40 1 13.41 3.84 2.12 18.8 90 2.45 \n", + "122 2 13.05 5.80 2.13 21.5 86 2.62 \n", + "0 1 NaN 1.78 2.14 11.2 100 2.65 \n", + ".. .. ... ... ... ... ... ... \n", + "156 3 12.45 3.03 2.64 27.0 97 1.90 \n", + "136 3 12.53 5.51 2.64 25.0 96 1.79 \n", + "158 3 13.48 1.67 2.64 22.5 89 2.60 \n", + "20 1 12.93 3.80 2.65 18.6 102 2.41 \n", + "33 1 13.51 1.80 2.65 19.0 110 2.35 \n", + "1 1 NaN 2.36 2.67 18.6 101 2.80 \n", + "70 2 13.86 1.51 2.67 25.0 86 2.95 \n", + "56 1 13.29 1.97 2.68 16.8 102 3.00 \n", + "52 1 13.77 1.90 2.68 17.1 115 3.00 \n", + "167 3 13.58 2.58 2.69 24.5 105 1.55 \n", + "29 1 13.73 1.50 2.70 22.5 101 3.00 \n", + "139 3 12.93 2.81 2.70 21.0 96 1.54 \n", + "14 1 13.63 1.81 2.70 17.2 112 2.85 \n", + "157 3 14.34 1.68 2.70 25.0 98 2.80 \n", + "32 1 13.76 1.53 2.70 19.5 132 2.95 \n", + "108 2 11.61 1.35 2.70 20.0 94 2.74 \n", + "141 3 13.52 3.17 2.72 23.5 97 1.55 \n", + "15 1 14.3 1.92 2.72 20.0 120 2.80 \n", + "121 2 12.42 4.43 2.73 26.5 102 2.20 \n", + "95 2 11.81 2.12 2.74 21.5 134 1.60 \n", + "176 3 14.13 4.10 2.74 24.5 96 2.05 \n", + "151 3 13.11 1.90 2.75 25.5 116 2.20 \n", + "126 2 11.79 2.13 2.78 28.5 92 2.13 \n", + "27 1 13.87 1.90 2.80 19.4 107 2.95 \n", + "35 1 13.28 1.64 2.84 15.5 110 2.60 \n", + "168 3 13.4 4.60 2.86 25.0 112 1.98 \n", + "3 1 13.24 2.59 2.87 21.0 118 2.80 \n", + "111 2 11.76 2.68 2.92 20.0 103 1.75 \n", + "24 1 13.05 2.05 3.22 25.0 124 2.63 \n", + "120 2 11.56 2.05 3.23 28.5 119 3.18 \n", + "\n", + " Flavanoids Nonflavonoid phenols Proanthocyanins Color intensity Hue \\\n", + "58 0.57 0.28 0.42 1.95 1.05 \n", + "99 2.17 0.26 1.40 3.30 1.27 \n", + "65 3.18 0.26 2.28 5.30 1.12 \n", + "75 2.03 0.24 1.46 4.60 1.19 \n", + "68 1.28 0.14 2.50 2.85 1.28 \n", + "109 2.58 0.24 3.58 2.90 0.75 \n", + "102 1.64 0.37 1.42 2.06 0.94 \n", + "100 1.36 0.29 1.35 2.45 1.04 \n", + "61 1.79 0.32 0.73 3.80 1.23 \n", + "66 2.00 0.27 1.04 4.68 1.12 \n", + "74 1.57 0.34 1.15 3.80 1.23 \n", + "107 2.04 0.39 2.08 2.70 0.86 \n", + "77 1.85 0.35 2.76 3.40 1.06 \n", + "103 1.92 0.32 1.48 2.94 1.04 \n", + "117 1.25 0.43 0.83 3.40 0.70 \n", + "96 2.50 0.29 1.77 2.90 1.23 \n", + "115 1.60 0.30 1.53 1.95 0.95 \n", + "118 1.64 0.37 1.87 1.28 0.93 \n", + "79 2.26 0.30 1.43 2.50 1.38 \n", + "60 1.41 0.53 0.62 5.75 0.98 \n", + "49 3.27 0.17 2.91 7.20 1.12 \n", + "97 3.75 0.24 1.95 4.50 1.04 \n", + "153 0.58 0.53 1.40 7.60 0.58 \n", + "37 2.64 0.28 1.37 3.70 1.18 \n", + "43 3.00 0.28 2.03 5.04 0.88 \n", + "46 3.39 0.21 2.14 6.10 0.91 \n", + "105 2.03 0.37 1.63 3.40 1.00 \n", + "40 2.68 0.27 1.48 4.28 0.91 \n", + "122 2.65 0.30 2.01 2.60 0.73 \n", + "0 2.76 0.26 1.28 4.38 1.05 \n", + ".. ... ... ... ... ... \n", + "156 0.58 0.63 1.14 7.50 0.67 \n", + "136 0.60 0.63 1.10 5.00 0.82 \n", + "158 1.10 0.52 2.29 11.75 0.57 \n", + "20 2.41 0.25 1.98 4.50 1.03 \n", + "33 2.53 0.29 1.54 4.20 1.10 \n", + "1 3.24 0.30 2.81 5.68 1.03 \n", + "70 2.86 0.21 1.87 3.38 1.36 \n", + "56 3.23 0.31 1.66 6.00 1.07 \n", + "52 2.79 0.39 1.68 6.30 1.13 \n", + "167 0.84 0.39 1.54 8.66 0.74 \n", + "29 3.25 0.29 2.38 5.70 1.19 \n", + "139 0.50 0.53 0.75 4.60 0.77 \n", + "14 2.91 0.30 1.46 7.30 1.28 \n", + "157 1.31 0.53 2.70 13.00 0.57 \n", + "32 2.74 0.50 1.35 5.40 1.25 \n", + "108 2.92 0.29 2.49 2.65 0.96 \n", + "141 0.52 0.50 0.55 4.35 0.89 \n", + "15 3.14 0.33 1.97 6.20 1.07 \n", + "121 2.13 0.43 1.71 2.08 0.92 \n", + "95 0.99 0.14 1.56 2.50 0.95 \n", + "176 0.76 0.56 1.35 9.20 0.61 \n", + "151 1.28 0.26 1.56 7.10 0.61 \n", + "126 2.24 0.58 1.76 3.00 0.97 \n", + "27 2.97 0.37 1.76 4.50 1.25 \n", + "35 2.68 0.34 1.36 4.60 1.09 \n", + "168 0.96 0.27 1.11 8.50 0.67 \n", + "3 2.69 0.39 1.82 4.32 1.04 \n", + "111 2.03 0.60 1.05 3.80 1.23 \n", + "24 2.68 0.47 1.92 3.58 1.13 \n", + "120 5.08 0.47 1.87 6.00 0.93 \n", + "\n", + " OD280/OD315 of diluted wines Proline \n", + "58 1.82 520 \n", + "99 2.96 710 \n", + "65 3.18 502 \n", + "75 2.48 392 \n", + "68 3.07 718 \n", + "109 2.81 562 \n", + "102 2.44 415 \n", + "100 2.77 562 \n", + "61 2.46 630 \n", + "66 3.48 510 \n", + "74 2.14 428 \n", + "107 3.02 312 \n", + "77 2.31 750 \n", + "103 3.57 672 \n", + "117 2.12 372 \n", + "96 2.74 428 \n", + "115 3.33 495 \n", + "118 3.05 564 \n", + "79 3.12 278 \n", + "60 1.59 450 \n", + "49 2.91 1150 \n", + "97 2.77 660 \n", + "153 1.55 640 \n", + "37 2.69 1020 \n", + "43 3.35 885 \n", + "46 3.33 985 \n", + "105 3.17 510 \n", + "40 3.00 1035 \n", + "122 3.10 380 \n", + "0 3.40 1050 \n", + ".. ... ... \n", + "156 1.73 880 \n", + "136 1.69 515 \n", + "158 1.78 620 \n", + "20 3.52 770 \n", + "33 2.87 1095 \n", + "1 3.17 1185 \n", + "70 3.16 410 \n", + "56 2.84 1270 \n", + "52 2.93 1375 \n", + "167 1.80 750 \n", + "29 2.71 1285 \n", + "139 2.31 600 \n", + "14 2.88 1310 \n", + "157 1.96 660 \n", + "32 3.00 1235 \n", + "108 3.26 680 \n", + "141 2.06 520 \n", + "15 2.65 1280 \n", + "121 3.12 365 \n", + "95 2.26 625 \n", + "176 1.60 560 \n", + "151 1.33 425 \n", + "126 2.44 466 \n", + "27 3.40 915 \n", + "35 2.78 880 \n", + "168 1.92 630 \n", + "3 2.93 735 \n", + "111 2.50 607 \n", + "24 3.20 830 \n", + "120 3.69 465 \n", + "\n", + "[177 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 39 + } + ] + }, + { + "metadata": { + "id": "HZZ-RG3mL8xe", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/Panda_2_0.ipynb b/Panda_2_0.ipynb new file mode 100644 index 0000000..243fe12 --- /dev/null +++ b/Panda_2_0.ipynb @@ -0,0 +1,2122 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Panda 2.0.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "Fa_Dan9mFytP", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "NdxT4oPIF43q", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "iris_df = pd.read_csv('https://raw.githubusercontent.com/uiuc-cse/data-fa14/gh-pages/data/iris.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "D64binzbGMLs", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "cb0cfece-3fd9-46df-fbc0-ca8b45b373ce" + }, + "cell_type": "code", + "source": [ + "iris_df.head()" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
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24.73.21.30.2setosa
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" + ], + "text/plain": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "ZBdyS7lXGRTX", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "e59326b4-23df-405b-b3e0-39d023d5e69a" + }, + "cell_type": "code", + "source": [ + "print(iris_df.shape)\n", + "print(iris_df.shape[0])\n", + "print(iris_df.shape[1])" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "(150, 5)\n", + "150\n", + "5\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "pn-Ni-uYGcFq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "69328f34-9feb-44e0-ba4f-aa33f0906b8a" + }, + "cell_type": "code", + "source": [ + "print(iris_df.columns)" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',\n", + " 'species'],\n", + " dtype='object')\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "U-7fUFVrGfbN", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "da2b5764-5fc2-48e9-fa0b-5f635caae756" + }, + "cell_type": "code", + "source": [ + "print(iris_df.index)" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RangeIndex(start=0, stop=150, step=1)\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "-unsLymKGnLZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "a5438b85-4790-4820-d7e2-c1c857d9e70b" + }, + "cell_type": "code", + "source": [ + "print(iris_df.head())\n", + "\n", + "\n", + "iris_df = iris_df.reindex(index = np.random.permutation(iris_df.index))\n", + "\n", + "print(iris_df.head())" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "0 5.1 3.5 1.4 0.2 setosa\n", + "1 4.9 3.0 1.4 0.2 setosa\n", + "2 4.7 3.2 1.3 0.2 setosa\n", + "3 4.6 3.1 1.5 0.2 setosa\n", + "4 5.0 3.6 1.4 0.2 setosa\n", + " sepal_length sepal_width petal_length petal_width species\n", + "129 7.2 3.0 5.8 1.6 virginica\n", + "35 5.0 3.2 1.2 0.2 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "49 5.0 3.3 1.4 0.2 setosa\n", + "21 5.1 3.7 1.5 0.4 setosa\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "znMECIQ-GwBb", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 323 + }, + "outputId": "4aeab003-6258-4d19-aa66-d478d87ce5e1" + }, + "cell_type": "code", + "source": [ + "#original\n", + "\n", + "print(iris_df.head())\n", + "\n", + "iris_df['sepal_width'] *= 10\n", + "\n", + "#changed\n", + "\n", + "print(iris_df.head())\n", + "\n", + "#lets undo the operation\n", + "\n", + "iris_df['sepal_width'] /= 10\n", + "\n", + "print(iris_df.head())" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + " sepal_length sepal_width petal_length petal_width species\n", + "129 7.2 3.0 5.8 1.6 virginica\n", + "35 5.0 3.2 1.2 0.2 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "49 5.0 3.3 1.4 0.2 setosa\n", + "21 5.1 3.7 1.5 0.4 setosa\n", + " sepal_length sepal_width petal_length petal_width species\n", + "129 7.2 30.0 5.8 1.6 virginica\n", + "35 5.0 32.0 1.2 0.2 setosa\n", + "24 4.8 34.0 1.9 0.2 setosa\n", + "49 5.0 33.0 1.4 0.2 setosa\n", + "21 5.1 37.0 1.5 0.4 setosa\n", + " sepal_length sepal_width petal_length petal_width species\n", + "129 7.2 3.0 5.8 1.6 virginica\n", + "35 5.0 3.2 1.2 0.2 setosa\n", + "24 4.8 3.4 1.9 0.2 setosa\n", + "49 5.0 3.3 1.4 0.2 setosa\n", + "21 5.1 3.7 1.5 0.4 setosa\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "n4zcGYPJG2Iq", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1165 + }, + "outputId": "90539777-ca37-4387-b047-3163dd30fc49" + }, + "cell_type": "code", + "source": [ + "iris_df[iris_df['sepal_width']>3.3]" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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244.83.41.90.2setosa
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45.03.61.40.2setosa
1366.33.45.62.4virginica
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224.63.61.00.2setosa
395.13.41.50.2setosa
275.23.51.50.2setosa
155.74.41.50.4setosa
145.84.01.20.2setosa
75.03.41.50.2setosa
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1097.23.66.12.5virginica
1317.93.86.42.0virginica
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175.13.51.40.3setosa
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315.43.41.50.4setosa
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405.03.51.30.3setosa
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64.63.41.40.3setosa
335.54.21.40.2setosa
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1486.23.45.42.3virginica
365.53.51.30.2setosa
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574.92.43.31.0versicolor
805.52.43.81.1versicolor
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1015.82.75.11.9virginica
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..................
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1177.73.86.72.2virginica
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