├── LICENSE ├── README.md ├── data ├── macrodata.csv ├── stock_px.csv ├── tips.csv └── 個股_類別.rar ├── jpgs └── MyPicture1.jpg └── notebooks ├── 0. Pandas入門介紹.ipynb ├── 1. Pandas - main classes and structure.ipynb ├── 2. Pandas - IO tools ├── 2. Pandas - IO tools.ipynb ├── ex1.csv ├── ex1.pickle ├── ex2.csv ├── ex3 - 1.csv ├── ex3.csv ├── ex4.csv ├── ex5-1.csv ├── ex5.csv ├── ex6-o.csv ├── ex6.csv ├── mta.xml ├── mydata.h5 ├── test.xls └── treseries.csv ├── 3. Pandas - ETL tools.ipynb ├── 4. Pandas - plotting.ipynb ├── 5. Pandas - GroupBy.ipynb ├── 練習 - 股票資料彙整_YahooFinance - 問題.ipynb ├── 練習 - 股票資料彙整_YahooFinance - 解答.ipynb ├── 練習 - 股票資料彙整_Yahoo股市 - 問題.ipynb └── 練習 - 股票資料彙整_Yahoo股市 - 解答.ipynb /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. By contrast, 15 | the GNU General Public License is intended to guarantee your freedom to 16 | share and change all versions of a program--to make sure it remains free 17 | software for all its users. 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Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | Wei Lin / Introduction to Pandas 635 | Copyright (C) 2016 Wei Lin 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Introduction to Pandas Copyright (C) 2016 Wei Lin 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Pandas 入門介紹 3 | Taichung.py 4 | 2016/3/12 5 | Wei Lin 6 | [Wei1234c@gmail.com](mailto://wei1234c@gmail.com) 7 | 8 | ## Books: 9 | [Python for Data Analysis](http://www.books.com.tw/products/F012771443) 10 |
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11 | 12 | ## Videos: 13 | [Data analysis in Python with pandas - Wes McKinney (Pandas之父)](https://www.youtube.com/watch?v=w26x-z-BdWQ) 14 | [Analyzing data with Pandas - PyCon SE 2015](https://www.youtube.com/watch?v=kSM8S76qYz0) 15 | [Pandas From The Ground Up - PyCon 2015](https://www.youtube.com/watch?v=5JnMutdy6Fw) / [Brandon’s Pandas Tutorial](https://github.com/brandon-rhodes/pycon-pandas-tutorial) 16 | [Hands-on Data Analysis with Python - PyCon 2015](https://www.youtube.com/watch?v=L4Hbv4ugUWk&list=PLHJdMADCPuGQeXXvSJnXGNhvYoOwcXjUD&index=4) 17 | 18 | ## Documentation: 19 | [pandas documentation — API Reference](http://pandas.pydata.org/pandas-docs/stable/api.html) 20 | 21 | ## About Me 22 | - Wei Lin : [Wei1234c@gmail.com](mailto://Wei1234c@gmail.com) 23 | - A planner in private enterprises 24 | - Started learning Python in 2015 25 | - Interested in Data-Science and A.I. 26 | 27 | ## About this talk 28 | 1. [Pandas - main classes and structure](https://github.com/Wei1234c/Introduction_to_Pandas/blob/master/notebooks/1.%20Pandas%20-%20main%20classes%20and%20structure.ipynb) 29 | 2. [Pandas - I/O tools](https://github.com/Wei1234c/Introduction_to_Pandas/blob/master/notebooks/2.%20Pandas%20-%20IO%20tools/2.%20Pandas%20-%20IO%20tools.ipynb) 30 | 3. [Pandas - ETL tools](https://github.com/Wei1234c/Introduction_to_Pandas/blob/master/notebooks/3.%20Pandas%20-%20ETL%20tools.ipynb) 31 | 4. [Pandas - plotting](https://github.com/Wei1234c/Introduction_to_Pandas/blob/master/notebooks/4.%20Pandas%20-%20plotting.ipynb) 32 | 5. [Pandas - GroupBy](https://github.com/Wei1234c/Introduction_to_Pandas/blob/master/notebooks/5.%20Pandas%20-%20GroupBy.ipynb) 33 | 34 | ## Design of this talk 35 | 1. Theory and Practice 36 | 2. Learning a language 37 | 3. What if ... 38 | -------------------------------------------------------------------------------- /data/macrodata.csv: -------------------------------------------------------------------------------- 1 | year,quarter,realgdp,realcons,realinv,realgovt,realdpi,cpi,m1,tbilrate,unemp,pop,infl,realint 2 | 1959.0,1.0,2710.349,1707.4,286.898,470.045,1886.9,28.98,139.7,2.82,5.8,177.146,0.0,0.0 3 | 1959.0,2.0,2778.801,1733.7,310.859,481.301,1919.7,29.15,141.7,3.08,5.1,177.83,2.34,0.74 4 | 1959.0,3.0,2775.488,1751.8,289.226,491.26,1916.4,29.35,140.5,3.82,5.3,178.657,2.74,1.09 5 | 1959.0,4.0,2785.204,1753.7,299.356,484.052,1931.3,29.37,140.0,4.33,5.6,179.386,0.27,4.06 6 | 1960.0,1.0,2847.699,1770.5,331.722,462.199,1955.5,29.54,139.6,3.5,5.2,180.007,2.31,1.19 7 | 1960.0,2.0,2834.39,1792.9,298.152,460.4,1966.1,29.55,140.2,2.68,5.2,180.671,0.14,2.55 8 | 1960.0,3.0,2839.022,1785.8,296.375,474.676,1967.8,29.75,140.9,2.36,5.6,181.528,2.7,-0.34 9 | 1960.0,4.0,2802.616,1788.2,259.764,476.434,1966.6,29.84,141.1,2.29,6.3,182.287,1.21,1.08 10 | 1961.0,1.0,2819.264,1787.7,266.405,475.854,1984.5,29.81,142.1,2.37,6.8,182.992,-0.4,2.77 11 | 1961.0,2.0,2872.005,1814.3,286.246,480.328,2014.4,29.92,142.9,2.29,7.0,183.691,1.47,0.81 12 | 1961.0,3.0,2918.419,1823.1,310.227,493.828,2041.9,29.98,144.1,2.32,6.8,184.524,0.8,1.52 13 | 1961.0,4.0,2977.83,1859.6,315.463,502.521,2082.0,30.04,145.2,2.6,6.2,185.242,0.8,1.8 14 | 1962.0,1.0,3031.241,1879.4,334.271,520.96,2101.7,30.21,146.4,2.73,5.6,185.874,2.26,0.47 15 | 1962.0,2.0,3064.709,1902.5,331.039,523.066,2125.2,30.22,146.5,2.78,5.5,186.538,0.13,2.65 16 | 1962.0,3.0,3093.047,1917.9,336.962,538.838,2137.0,30.38,146.7,2.78,5.6,187.323,2.11,0.67 17 | 1962.0,4.0,3100.563,1945.1,325.65,535.912,2154.6,30.44,148.3,2.87,5.5,188.013,0.79,2.08 18 | 1963.0,1.0,3141.087,1958.2,343.721,522.917,2172.5,30.48,149.7,2.9,5.8,188.58,0.53,2.38 19 | 1963.0,2.0,3180.447,1976.9,348.73,518.108,2193.1,30.69,151.3,3.03,5.7,189.242,2.75,0.29 20 | 1963.0,3.0,3240.332,2003.8,360.102,546.893,2217.9,30.75,152.6,3.38,5.5,190.028,0.78,2.6 21 | 1963.0,4.0,3264.967,2020.6,364.534,532.383,2254.6,30.94,153.7,3.52,5.6,190.668,2.46,1.06 22 | 1964.0,1.0,3338.246,2060.5,379.523,529.686,2299.6,30.95,154.8,3.51,5.5,191.245,0.13,3.38 23 | 1964.0,2.0,3376.587,2096.7,377.778,526.175,2362.1,31.02,156.8,3.47,5.2,191.889,0.9,2.57 24 | 1964.0,3.0,3422.469,2135.2,386.754,522.008,2392.7,31.12,159.2,3.53,5.0,192.631,1.29,2.25 25 | 1964.0,4.0,3431.957,2141.2,389.91,514.603,2420.4,31.28,160.7,3.76,5.0,193.223,2.05,1.71 26 | 1965.0,1.0,3516.251,2188.8,429.145,508.006,2447.4,31.38,162.0,3.93,4.9,193.709,1.28,2.65 27 | 1965.0,2.0,3563.96,2213.0,429.119,508.931,2474.5,31.58,163.1,3.84,4.7,194.303,2.54,1.3 28 | 1965.0,3.0,3636.285,2251.0,444.444,529.446,2542.6,31.65,166.0,3.93,4.4,194.997,0.89,3.04 29 | 1965.0,4.0,3724.014,2314.3,446.493,544.121,2594.1,31.88,169.1,4.35,4.1,195.539,2.9,1.46 30 | 1966.0,1.0,3815.423,2348.5,484.244,556.593,2618.4,32.28,171.8,4.62,3.9,195.999,4.99,-0.37 31 | 1966.0,2.0,3828.124,2354.5,475.408,571.371,2624.7,32.45,170.3,4.65,3.8,196.56,2.1,2.55 32 | 1966.0,3.0,3853.301,2381.5,470.697,594.514,2657.8,32.85,171.2,5.23,3.8,197.207,4.9,0.33 33 | 1966.0,4.0,3884.52,2391.4,472.957,599.528,2688.2,32.9,171.9,5.0,3.7,197.736,0.61,4.39 34 | 1967.0,1.0,3918.74,2405.3,460.007,640.682,2728.4,33.1,174.2,4.22,3.8,198.206,2.42,1.8 35 | 1967.0,2.0,3919.556,2438.1,440.393,631.43,2750.8,33.4,178.1,3.78,3.8,198.712,3.61,0.17 36 | 1967.0,3.0,3950.826,2450.6,453.033,641.504,2777.1,33.7,181.6,4.42,3.8,199.311,3.58,0.84 37 | 1967.0,4.0,3980.97,2465.7,462.834,640.234,2797.4,34.1,184.3,4.9,3.9,199.808,4.72,0.18 38 | 1968.0,1.0,4063.013,2524.6,472.907,651.378,2846.2,34.4,186.6,5.18,3.7,200.208,3.5,1.67 39 | 1968.0,2.0,4131.998,2563.3,492.026,646.145,2893.5,34.9,190.5,5.5,3.5,200.706,5.77,-0.28 40 | 1968.0,3.0,4160.267,2611.5,476.053,640.615,2899.3,35.3,194.0,5.21,3.5,201.29,4.56,0.65 41 | 1968.0,4.0,4178.293,2623.5,480.998,636.729,2918.4,35.7,198.7,5.85,3.4,201.76,4.51,1.34 42 | 1969.0,1.0,4244.1,2652.9,512.686,633.224,2923.4,36.3,200.7,6.08,3.4,202.161,6.67,-0.58 43 | 1969.0,2.0,4256.46,2669.8,508.601,623.16,2952.9,36.8,201.7,6.49,3.4,202.677,5.47,1.02 44 | 1969.0,3.0,4283.378,2682.7,520.36,623.613,3012.9,37.3,202.9,7.02,3.6,203.302,5.4,1.63 45 | 1969.0,4.0,4263.261,2704.1,492.334,606.9,3034.9,37.9,206.2,7.64,3.6,203.849,6.38,1.26 46 | 1970.0,1.0,4256.573,2720.7,476.925,594.888,3050.1,38.5,206.7,6.76,4.2,204.401,6.28,0.47 47 | 1970.0,2.0,4264.289,2733.2,478.419,576.257,3103.5,38.9,208.0,6.66,4.8,205.052,4.13,2.52 48 | 1970.0,3.0,4302.259,2757.1,486.594,567.743,3145.4,39.4,212.9,6.15,5.2,205.788,5.11,1.04 49 | 1970.0,4.0,4256.637,2749.6,458.406,564.666,3135.1,39.9,215.5,4.86,5.8,206.466,5.04,-0.18 50 | 1971.0,1.0,4374.016,2802.2,517.935,542.709,3197.3,40.1,220.0,3.65,5.9,207.065,2.0,1.65 51 | 1971.0,2.0,4398.829,2827.9,533.986,534.905,3245.3,40.6,224.9,4.76,5.9,207.661,4.96,-0.19 52 | 1971.0,3.0,4433.943,2850.4,541.01,532.646,3259.7,40.9,227.2,4.7,6.0,208.345,2.94,1.75 53 | 1971.0,4.0,4446.264,2897.8,524.085,516.14,3294.2,41.2,230.1,3.87,6.0,208.917,2.92,0.95 54 | 1972.0,1.0,4525.769,2936.5,561.147,518.192,3314.9,41.5,235.6,3.55,5.8,209.386,2.9,0.64 55 | 1972.0,2.0,4633.101,2992.6,595.495,526.473,3346.1,41.8,238.8,3.86,5.7,209.896,2.88,0.98 56 | 1972.0,3.0,4677.503,3038.8,603.97,498.116,3414.6,42.2,245.0,4.47,5.6,210.479,3.81,0.66 57 | 1972.0,4.0,4754.546,3110.1,607.104,496.54,3550.5,42.7,251.5,5.09,5.3,210.985,4.71,0.38 58 | 1973.0,1.0,4876.166,3167.0,645.654,504.838,3590.7,43.7,252.7,5.98,5.0,211.42,9.26,-3.28 59 | 1973.0,2.0,4932.571,3165.4,675.837,497.033,3626.2,44.2,257.5,7.19,4.9,211.909,4.55,2.64 60 | 1973.0,3.0,4906.252,3176.7,649.412,475.897,3644.4,45.6,259.0,8.06,4.8,212.475,12.47,-4.41 61 | 1973.0,4.0,4953.05,3167.4,674.253,476.174,3688.9,46.8,263.8,7.68,4.8,212.932,10.39,-2.71 62 | 1974.0,1.0,4909.617,3139.7,631.23,491.043,3632.3,48.1,267.2,7.8,5.1,213.361,10.96,-3.16 63 | 1974.0,2.0,4922.188,3150.6,628.102,490.177,3601.1,49.3,269.3,7.89,5.2,213.854,9.86,-1.96 64 | 1974.0,3.0,4873.52,3163.6,592.672,492.586,3612.4,51.0,272.3,8.16,5.6,214.451,13.56,-5.4 65 | 1974.0,4.0,4854.34,3117.3,598.306,496.176,3596.0,52.3,273.9,6.96,6.6,214.931,10.07,-3.11 66 | 1975.0,1.0,4795.295,3143.4,493.212,490.603,3581.9,53.0,276.2,5.53,8.2,215.353,5.32,0.22 67 | 1975.0,2.0,4831.942,3195.8,476.085,486.679,3749.3,54.0,283.7,5.57,8.9,215.973,7.48,-1.91 68 | 1975.0,3.0,4913.328,3241.4,516.402,498.836,3698.6,54.9,285.4,6.27,8.5,216.587,6.61,-0.34 69 | 1975.0,4.0,4977.511,3275.7,530.596,500.141,3736.0,55.8,288.4,5.26,8.3,217.095,6.5,-1.24 70 | 1976.0,1.0,5090.663,3341.2,585.541,495.568,3791.0,56.1,294.7,4.91,7.7,217.528,2.14,2.77 71 | 1976.0,2.0,5128.947,3371.8,610.513,494.532,3822.2,57.0,297.2,5.28,7.6,218.035,6.37,-1.09 72 | 1976.0,3.0,5154.072,3407.5,611.646,493.141,3856.7,57.9,302.0,5.05,7.7,218.644,6.27,-1.22 73 | 1976.0,4.0,5191.499,3451.8,615.898,494.415,3884.4,58.7,308.3,4.57,7.8,219.179,5.49,-0.92 74 | 1977.0,1.0,5251.762,3491.3,646.198,498.509,3887.5,60.0,316.0,4.6,7.5,219.684,8.76,-4.16 75 | 1977.0,2.0,5356.131,3510.6,696.141,506.695,3931.8,60.8,320.2,5.06,7.1,220.239,5.3,-0.24 76 | 1977.0,3.0,5451.921,3544.1,734.078,509.605,3990.8,61.6,326.4,5.82,6.9,220.904,5.23,0.59 77 | 1977.0,4.0,5450.793,3597.5,713.356,504.584,4071.2,62.7,334.4,6.2,6.6,221.477,7.08,-0.88 78 | 1978.0,1.0,5469.405,3618.5,727.504,506.314,4096.4,63.9,339.9,6.34,6.3,221.991,7.58,-1.24 79 | 1978.0,2.0,5684.569,3695.9,777.454,518.366,4143.4,65.5,347.6,6.72,6.0,222.585,9.89,-3.18 80 | 1978.0,3.0,5740.3,3711.4,801.452,520.199,4177.1,67.1,353.3,7.64,6.0,223.271,9.65,-2.01 81 | 1978.0,4.0,5816.222,3741.3,819.689,524.782,4209.8,68.5,358.6,9.02,5.9,223.865,8.26,0.76 82 | 1979.0,1.0,5825.949,3760.2,819.556,525.524,4255.9,70.6,368.0,9.42,5.9,224.438,12.08,-2.66 83 | 1979.0,2.0,5831.418,3758.0,817.66,532.04,4226.1,73.0,377.2,9.3,5.7,225.055,13.37,-4.07 84 | 1979.0,3.0,5873.335,3794.9,801.742,531.232,4250.3,75.2,380.8,10.49,5.9,225.801,11.88,-1.38 85 | 1979.0,4.0,5889.495,3805.0,786.817,531.126,4284.3,78.0,385.8,11.94,5.9,226.451,14.62,-2.68 86 | 1980.0,1.0,5908.467,3798.4,781.114,548.115,4296.2,80.9,383.8,13.75,6.3,227.061,14.6,-0.85 87 | 1980.0,2.0,5787.373,3712.2,710.64,561.895,4236.1,82.6,394.0,7.9,7.3,227.726,8.32,-0.42 88 | 1980.0,3.0,5776.617,3752.0,656.477,554.292,4279.7,84.7,409.0,10.34,7.7,228.417,10.04,0.3 89 | 1980.0,4.0,5883.46,3802.0,723.22,556.13,4368.1,87.2,411.3,14.75,7.4,228.937,11.64,3.11 90 | 1981.0,1.0,6005.717,3822.8,795.091,567.618,4358.1,89.1,427.4,13.95,7.4,229.403,8.62,5.32 91 | 1981.0,2.0,5957.795,3822.8,757.24,584.54,4358.6,91.5,426.9,15.33,7.4,229.966,10.63,4.69 92 | 1981.0,3.0,6030.184,3838.3,804.242,583.89,4455.4,93.4,428.4,14.58,7.4,230.641,8.22,6.36 93 | 1981.0,4.0,5955.062,3809.3,773.053,590.125,4464.4,94.4,442.7,11.33,8.2,231.157,4.26,7.07 94 | 1982.0,1.0,5857.333,3833.9,692.514,591.043,4469.6,95.0,447.1,12.95,8.8,231.645,2.53,10.42 95 | 1982.0,2.0,5889.074,3847.7,691.9,596.403,4500.8,97.5,448.0,11.97,9.4,232.188,10.39,1.58 96 | 1982.0,3.0,5866.37,3877.2,683.825,605.37,4520.6,98.1,464.5,8.1,9.9,232.816,2.45,5.65 97 | 1982.0,4.0,5871.001,3947.9,622.93,623.307,4536.4,97.9,477.2,7.96,10.7,233.322,-0.82,8.77 98 | 1983.0,1.0,5944.02,3986.6,645.11,630.873,4572.2,98.8,493.2,8.22,10.4,233.781,3.66,4.56 99 | 1983.0,2.0,6077.619,4065.7,707.372,644.322,4605.5,99.8,507.8,8.69,10.1,234.307,4.03,4.66 100 | 1983.0,3.0,6197.468,4137.6,754.937,662.412,4674.7,100.8,517.2,8.99,9.4,234.907,3.99,5.01 101 | 1983.0,4.0,6325.574,4203.2,834.427,639.197,4771.1,102.1,525.1,8.89,8.5,235.385,5.13,3.76 102 | 1984.0,1.0,6448.264,4239.2,921.763,644.635,4875.4,103.3,535.0,9.43,7.9,235.839,4.67,4.76 103 | 1984.0,2.0,6559.594,4299.9,952.841,664.839,4959.4,104.1,540.9,9.94,7.5,236.348,3.09,6.85 104 | 1984.0,3.0,6623.343,4333.0,974.989,662.294,5036.6,105.1,543.7,10.19,7.4,236.976,3.82,6.37 105 | 1984.0,4.0,6677.264,4390.1,958.993,684.282,5084.5,105.7,557.0,8.14,7.3,237.468,2.28,5.87 106 | 1985.0,1.0,6740.275,4464.6,927.375,691.613,5072.0,107.0,570.4,8.25,7.3,237.9,4.89,3.36 107 | 1985.0,2.0,6797.344,4505.2,943.383,708.524,5172.7,107.7,589.1,7.17,7.3,238.466,2.61,4.56 108 | 1985.0,3.0,6903.523,4590.8,932.959,732.305,5140.7,108.5,607.8,7.13,7.2,239.113,2.96,4.17 109 | 1985.0,4.0,6955.918,4600.9,969.434,732.026,5193.9,109.9,621.4,7.14,7.0,239.638,5.13,2.01 110 | 1986.0,1.0,7022.757,4639.3,967.442,728.125,5255.8,108.7,641.0,6.56,7.0,240.094,-4.39,10.95 111 | 1986.0,2.0,7050.969,4688.7,945.972,751.334,5315.5,109.5,670.3,6.06,7.2,240.651,2.93,3.13 112 | 1986.0,3.0,7118.95,4770.7,916.315,779.77,5343.3,110.2,694.9,5.31,7.0,241.274,2.55,2.76 113 | 1986.0,4.0,7153.359,4799.4,917.736,767.671,5346.5,111.4,730.2,5.44,6.8,241.784,4.33,1.1 114 | 1987.0,1.0,7193.019,4792.1,945.776,772.247,5379.4,112.7,743.9,5.61,6.6,242.252,4.64,0.97 115 | 1987.0,2.0,7269.51,4856.3,947.1,782.962,5321.0,113.8,743.0,5.67,6.3,242.804,3.89,1.79 116 | 1987.0,3.0,7332.558,4910.4,948.055,783.804,5416.2,115.0,756.2,6.19,6.0,243.446,4.2,1.99 117 | 1987.0,4.0,7458.022,4922.2,1021.98,795.467,5493.1,116.0,756.2,5.76,5.9,243.981,3.46,2.29 118 | 1988.0,1.0,7496.6,5004.4,964.398,773.851,5562.1,117.2,768.1,5.76,5.7,244.445,4.12,1.64 119 | 1988.0,2.0,7592.881,5040.8,987.858,765.98,5614.3,118.5,781.4,6.48,5.5,245.021,4.41,2.07 120 | 1988.0,3.0,7632.082,5080.6,994.204,760.245,5657.5,119.9,783.3,7.22,5.5,245.693,4.7,2.52 121 | 1988.0,4.0,7733.991,5140.4,1007.371,783.065,5708.5,121.2,785.7,8.03,5.3,246.224,4.31,3.72 122 | 1989.0,1.0,7806.603,5159.3,1045.975,767.024,5773.4,123.1,779.2,8.67,5.2,246.721,6.22,2.44 123 | 1989.0,2.0,7865.016,5182.4,1033.753,784.275,5749.8,124.5,777.8,8.15,5.2,247.342,4.52,3.63 124 | 1989.0,3.0,7927.393,5236.1,1021.604,791.819,5787.0,125.4,786.6,7.76,5.3,248.067,2.88,4.88 125 | 1989.0,4.0,7944.697,5261.7,1011.119,787.844,5831.3,127.5,795.4,7.65,5.4,248.659,6.64,1.01 126 | 1990.0,1.0,8027.693,5303.3,1021.07,799.681,5875.1,128.9,806.2,7.8,5.3,249.306,4.37,3.44 127 | 1990.0,2.0,8059.598,5320.8,1021.36,800.639,5913.9,130.5,810.1,7.7,5.3,250.132,4.93,2.76 128 | 1990.0,3.0,8059.476,5341.0,997.319,793.513,5918.1,133.4,819.8,7.33,5.7,251.057,8.79,-1.46 129 | 1990.0,4.0,7988.864,5299.5,934.248,800.525,5878.2,134.7,827.2,6.67,6.1,251.889,3.88,2.79 130 | 1991.0,1.0,7950.164,5284.4,896.21,806.775,5896.3,135.1,843.2,5.83,6.6,252.643,1.19,4.65 131 | 1991.0,2.0,8003.822,5324.7,891.704,809.081,5941.1,136.2,861.5,5.54,6.8,253.493,3.24,2.29 132 | 1991.0,3.0,8037.538,5345.0,913.904,793.987,5953.6,137.2,878.0,5.18,6.9,254.435,2.93,2.25 133 | 1991.0,4.0,8069.046,5342.6,948.891,778.378,5992.4,138.3,910.4,4.14,7.1,255.214,3.19,0.95 134 | 1992.0,1.0,8157.616,5434.5,927.796,778.568,6082.9,139.4,943.8,3.88,7.4,255.992,3.17,0.71 135 | 1992.0,2.0,8244.294,5466.7,988.912,777.762,6129.5,140.5,963.2,3.5,7.6,256.894,3.14,0.36 136 | 1992.0,3.0,8329.361,5527.1,999.135,786.639,6160.6,141.7,1003.8,2.97,7.6,257.861,3.4,-0.44 137 | 1992.0,4.0,8417.016,5594.6,1030.758,787.064,6248.2,142.8,1030.4,3.12,7.4,258.679,3.09,0.02 138 | 1993.0,1.0,8432.485,5617.2,1054.979,762.901,6156.5,143.8,1047.6,2.92,7.2,259.414,2.79,0.13 139 | 1993.0,2.0,8486.435,5671.1,1063.263,752.158,6252.3,144.5,1084.5,3.02,7.1,260.255,1.94,1.08 140 | 1993.0,3.0,8531.108,5732.7,1062.514,744.227,6265.7,145.6,1113.0,3.0,6.8,261.163,3.03,-0.04 141 | 1993.0,4.0,8643.769,5783.7,1118.583,748.102,6358.1,146.3,1131.6,3.05,6.6,261.919,1.92,1.13 142 | 1994.0,1.0,8727.919,5848.1,1166.845,721.288,6332.6,147.2,1141.1,3.48,6.6,262.631,2.45,1.02 143 | 1994.0,2.0,8847.303,5891.5,1234.855,717.197,6440.6,148.4,1150.5,4.2,6.2,263.436,3.25,0.96 144 | 1994.0,3.0,8904.289,5938.7,1212.655,736.89,6487.9,149.4,1150.1,4.68,6.0,264.301,2.69,2.0 145 | 1994.0,4.0,9003.18,5997.3,1269.19,716.702,6574.0,150.5,1151.4,5.53,5.6,265.044,2.93,2.6 146 | 1995.0,1.0,9025.267,6004.3,1282.09,715.326,6616.6,151.8,1149.3,5.72,5.5,265.755,3.44,2.28 147 | 1995.0,2.0,9044.668,6053.5,1247.61,712.492,6617.2,152.6,1145.4,5.52,5.7,266.557,2.1,3.42 148 | 1995.0,3.0,9120.684,6107.6,1235.601,707.649,6666.8,153.5,1137.3,5.32,5.7,267.456,2.35,2.97 149 | 1995.0,4.0,9184.275,6150.6,1270.392,681.081,6706.2,154.7,1123.5,5.17,5.6,268.151,3.11,2.05 150 | 1996.0,1.0,9247.188,6206.9,1287.128,695.265,6777.7,156.1,1124.8,4.91,5.5,268.853,3.6,1.31 151 | 1996.0,2.0,9407.052,6277.1,1353.795,705.172,6850.6,157.0,1112.4,5.09,5.5,269.667,2.3,2.79 152 | 1996.0,3.0,9488.879,6314.6,1422.059,692.741,6908.9,158.2,1086.1,5.04,5.3,270.581,3.05,2.0 153 | 1996.0,4.0,9592.458,6366.1,1418.193,690.744,6946.8,159.4,1081.5,4.99,5.3,271.36,3.02,1.97 154 | 1997.0,1.0,9666.235,6430.2,1451.304,681.445,7008.9,159.9,1063.8,5.1,5.2,272.083,1.25,3.85 155 | 1997.0,2.0,9809.551,6456.2,1543.976,693.525,7061.5,160.4,1066.2,5.01,5.0,272.912,1.25,3.76 156 | 1997.0,3.0,9932.672,6566.0,1571.426,691.261,7142.4,161.5,1065.5,5.02,4.9,273.852,2.73,2.29 157 | 1997.0,4.0,10008.874,6641.1,1596.523,690.311,7241.5,162.0,1074.4,5.11,4.7,274.626,1.24,3.88 158 | 1998.0,1.0,10103.425,6707.2,1672.732,668.783,7406.2,162.2,1076.1,5.02,4.6,275.304,0.49,4.53 159 | 1998.0,2.0,10194.277,6822.6,1652.716,687.184,7512.0,163.2,1075.0,4.98,4.4,276.115,2.46,2.52 160 | 1998.0,3.0,10328.787,6913.1,1700.071,681.472,7591.0,163.9,1086.0,4.49,4.5,277.003,1.71,2.78 161 | 1998.0,4.0,10507.575,7019.1,1754.743,688.147,7646.5,164.7,1097.8,4.38,4.4,277.79,1.95,2.43 162 | 1999.0,1.0,10601.179,7088.3,1809.993,683.601,7698.4,165.9,1101.9,4.39,4.3,278.451,2.9,1.49 163 | 1999.0,2.0,10684.049,7199.9,1803.674,683.594,7716.0,166.7,1098.7,4.54,4.3,279.295,1.92,2.62 164 | 1999.0,3.0,10819.914,7286.4,1848.949,697.936,7765.9,168.1,1102.3,4.75,4.2,280.203,3.35,1.41 165 | 1999.0,4.0,11014.254,7389.2,1914.567,713.445,7887.7,169.3,1121.9,5.2,4.1,280.976,2.85,2.35 166 | 2000.0,1.0,11043.044,7501.3,1887.836,685.216,8053.4,170.9,1113.5,5.63,4.0,281.653,3.76,1.87 167 | 2000.0,2.0,11258.454,7571.8,2018.529,712.641,8135.9,172.7,1103.0,5.81,3.9,282.385,4.19,1.62 168 | 2000.0,3.0,11267.867,7645.9,1986.956,698.827,8222.3,173.9,1098.7,6.07,4.0,283.19,2.77,3.3 169 | 2000.0,4.0,11334.544,7713.5,1987.845,695.597,8234.6,175.6,1097.7,5.7,3.9,283.9,3.89,1.81 170 | 2001.0,1.0,11297.171,7744.3,1882.691,710.403,8296.5,176.4,1114.9,4.39,4.2,284.55,1.82,2.57 171 | 2001.0,2.0,11371.251,7773.5,1876.65,725.623,8273.7,177.4,1139.7,3.54,4.4,285.267,2.26,1.28 172 | 2001.0,3.0,11340.075,7807.7,1837.074,730.493,8484.5,177.6,1166.0,2.72,4.8,286.047,0.45,2.27 173 | 2001.0,4.0,11380.128,7930.0,1731.189,739.318,8385.5,177.7,1190.9,1.74,5.5,286.728,0.23,1.51 174 | 2002.0,1.0,11477.868,7957.3,1789.327,756.915,8611.6,179.3,1185.9,1.75,5.7,287.328,3.59,-1.84 175 | 2002.0,2.0,11538.77,7997.8,1810.779,774.408,8658.9,180.0,1199.5,1.7,5.8,288.028,1.56,0.14 176 | 2002.0,3.0,11596.43,8052.0,1814.531,786.673,8629.2,181.2,1204.0,1.61,5.7,288.783,2.66,-1.05 177 | 2002.0,4.0,11598.824,8080.6,1813.219,799.967,8649.6,182.6,1226.8,1.2,5.8,289.421,3.08,-1.88 178 | 2003.0,1.0,11645.819,8122.3,1813.141,800.196,8681.3,183.2,1248.4,1.14,5.9,290.019,1.31,-0.17 179 | 2003.0,2.0,11738.706,8197.8,1823.698,838.775,8812.5,183.7,1287.9,0.96,6.2,290.704,1.09,-0.13 180 | 2003.0,3.0,11935.461,8312.1,1889.883,839.598,8935.4,184.9,1297.3,0.94,6.1,291.449,2.6,-1.67 181 | 2003.0,4.0,12042.817,8358.0,1959.783,845.722,8986.4,186.3,1306.1,0.9,5.8,292.057,3.02,-2.11 182 | 2004.0,1.0,12127.623,8437.6,1970.015,856.57,9025.9,187.4,1332.1,0.94,5.7,292.635,2.35,-1.42 183 | 2004.0,2.0,12213.818,8483.2,2055.58,861.44,9115.0,189.1,1340.5,1.21,5.6,293.31,3.61,-2.41 184 | 2004.0,3.0,12303.533,8555.8,2082.231,876.385,9175.9,190.8,1361.0,1.63,5.4,294.066,3.58,-1.95 185 | 2004.0,4.0,12410.282,8654.2,2125.152,865.596,9303.4,191.8,1366.6,2.2,5.4,294.741,2.09,0.11 186 | 2005.0,1.0,12534.113,8719.0,2170.299,869.204,9189.6,193.8,1357.8,2.69,5.3,295.308,4.15,-1.46 187 | 2005.0,2.0,12587.535,8802.9,2131.468,870.044,9253.0,194.7,1366.6,3.01,5.1,295.994,1.85,1.16 188 | 2005.0,3.0,12683.153,8865.6,2154.949,890.394,9308.0,199.2,1375.0,3.52,5.0,296.77,9.14,-5.62 189 | 2005.0,4.0,12748.699,8888.5,2232.193,875.557,9358.7,199.4,1380.6,4.0,4.9,297.435,0.4,3.6 190 | 2006.0,1.0,12915.938,8986.6,2264.721,900.511,9533.8,200.7,1380.5,4.51,4.7,298.061,2.6,1.91 191 | 2006.0,2.0,12962.462,9035.0,2261.247,892.839,9617.3,202.7,1369.2,4.82,4.7,298.766,3.97,0.85 192 | 2006.0,3.0,12965.916,9090.7,2229.636,892.002,9662.5,201.9,1369.4,4.9,4.7,299.593,-1.58,6.48 193 | 2006.0,4.0,13060.679,9181.6,2165.966,894.404,9788.8,203.574,1373.6,4.92,4.4,300.32,3.3,1.62 194 | 2007.0,1.0,13099.901,9265.1,2132.609,882.766,9830.2,205.92,1379.7,4.95,4.5,300.977,4.58,0.36 195 | 2007.0,2.0,13203.977,9291.5,2162.214,898.713,9842.7,207.338,1370.0,4.72,4.5,301.714,2.75,1.97 196 | 2007.0,3.0,13321.109,9335.6,2166.491,918.983,9883.9,209.133,1379.2,4.0,4.7,302.509,3.45,0.55 197 | 2007.0,4.0,13391.249,9363.6,2123.426,925.11,9886.2,212.495,1377.4,3.01,4.8,303.204,6.38,-3.37 198 | 2008.0,1.0,13366.865,9349.6,2082.886,943.372,9826.8,213.997,1384.0,1.56,4.9,303.803,2.82,-1.26 199 | 2008.0,2.0,13415.266,9351.0,2026.518,961.28,10059.0,218.61,1409.3,1.74,5.4,304.483,8.53,-6.79 200 | 2008.0,3.0,13324.6,9267.7,1990.693,991.551,9838.3,216.889,1474.7,1.17,6.0,305.27,-3.16,4.33 201 | 2008.0,4.0,13141.92,9195.3,1857.661,1007.273,9920.4,212.174,1576.5,0.12,6.9,305.952,-8.79,8.91 202 | 2009.0,1.0,12925.41,9209.2,1558.494,996.287,9926.4,212.671,1592.8,0.22,8.1,306.547,0.94,-0.71 203 | 2009.0,2.0,12901.504,9189.0,1456.678,1023.528,10077.5,214.469,1653.6,0.18,9.2,307.226,3.37,-3.19 204 | 2009.0,3.0,12990.341,9256.0,1486.398,1044.088,10040.6,216.385,1673.9,0.12,9.6,308.013,3.56,-3.44 205 | -------------------------------------------------------------------------------- /data/tips.csv: -------------------------------------------------------------------------------- 1 | total_bill,tip,sex,smoker,day,time,size 2 | 16.99,1.01,Female,No,Sun,Dinner,2 3 | 10.34,1.66,Male,No,Sun,Dinner,3 4 | 21.01,3.5,Male,No,Sun,Dinner,3 5 | 23.68,3.31,Male,No,Sun,Dinner,2 6 | 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15.98,2.03,Male,No,Thur,Lunch,2 87 | 34.83,5.17,Female,No,Thur,Lunch,4 88 | 13.03,2.0,Male,No,Thur,Lunch,2 89 | 18.28,4.0,Male,No,Thur,Lunch,2 90 | 24.71,5.85,Male,No,Thur,Lunch,2 91 | 21.16,3.0,Male,No,Thur,Lunch,2 92 | 28.97,3.0,Male,Yes,Fri,Dinner,2 93 | 22.49,3.5,Male,No,Fri,Dinner,2 94 | 5.75,1.0,Female,Yes,Fri,Dinner,2 95 | 16.32,4.3,Female,Yes,Fri,Dinner,2 96 | 22.75,3.25,Female,No,Fri,Dinner,2 97 | 40.17,4.73,Male,Yes,Fri,Dinner,4 98 | 27.28,4.0,Male,Yes,Fri,Dinner,2 99 | 12.03,1.5,Male,Yes,Fri,Dinner,2 100 | 21.01,3.0,Male,Yes,Fri,Dinner,2 101 | 12.46,1.5,Male,No,Fri,Dinner,2 102 | 11.35,2.5,Female,Yes,Fri,Dinner,2 103 | 15.38,3.0,Female,Yes,Fri,Dinner,2 104 | 44.3,2.5,Female,Yes,Sat,Dinner,3 105 | 22.42,3.48,Female,Yes,Sat,Dinner,2 106 | 20.92,4.08,Female,No,Sat,Dinner,2 107 | 15.36,1.64,Male,Yes,Sat,Dinner,2 108 | 20.49,4.06,Male,Yes,Sat,Dinner,2 109 | 25.21,4.29,Male,Yes,Sat,Dinner,2 110 | 18.24,3.76,Male,No,Sat,Dinner,2 111 | 14.31,4.0,Female,Yes,Sat,Dinner,2 112 | 14.0,3.0,Male,No,Sat,Dinner,2 113 | 7.25,1.0,Female,No,Sat,Dinner,1 114 | 38.07,4.0,Male,No,Sun,Dinner,3 115 | 23.95,2.55,Male,No,Sun,Dinner,2 116 | 25.71,4.0,Female,No,Sun,Dinner,3 117 | 17.31,3.5,Female,No,Sun,Dinner,2 118 | 29.93,5.07,Male,No,Sun,Dinner,4 119 | 10.65,1.5,Female,No,Thur,Lunch,2 120 | 12.43,1.8,Female,No,Thur,Lunch,2 121 | 24.08,2.92,Female,No,Thur,Lunch,4 122 | 11.69,2.31,Male,No,Thur,Lunch,2 123 | 13.42,1.68,Female,No,Thur,Lunch,2 124 | 14.26,2.5,Male,No,Thur,Lunch,2 125 | 15.95,2.0,Male,No,Thur,Lunch,2 126 | 12.48,2.52,Female,No,Thur,Lunch,2 127 | 29.8,4.2,Female,No,Thur,Lunch,6 128 | 8.52,1.48,Male,No,Thur,Lunch,2 129 | 14.52,2.0,Female,No,Thur,Lunch,2 130 | 11.38,2.0,Female,No,Thur,Lunch,2 131 | 22.82,2.18,Male,No,Thur,Lunch,3 132 | 19.08,1.5,Male,No,Thur,Lunch,2 133 | 20.27,2.83,Female,No,Thur,Lunch,2 134 | 11.17,1.5,Female,No,Thur,Lunch,2 135 | 12.26,2.0,Female,No,Thur,Lunch,2 136 | 18.26,3.25,Female,No,Thur,Lunch,2 137 | 8.51,1.25,Female,No,Thur,Lunch,2 138 | 10.33,2.0,Female,No,Thur,Lunch,2 139 | 14.15,2.0,Female,No,Thur,Lunch,2 140 | 16.0,2.0,Male,Yes,Thur,Lunch,2 141 | 13.16,2.75,Female,No,Thur,Lunch,2 142 | 17.47,3.5,Female,No,Thur,Lunch,2 143 | 34.3,6.7,Male,No,Thur,Lunch,6 144 | 41.19,5.0,Male,No,Thur,Lunch,5 145 | 27.05,5.0,Female,No,Thur,Lunch,6 146 | 16.43,2.3,Female,No,Thur,Lunch,2 147 | 8.35,1.5,Female,No,Thur,Lunch,2 148 | 18.64,1.36,Female,No,Thur,Lunch,3 149 | 11.87,1.63,Female,No,Thur,Lunch,2 150 | 9.78,1.73,Male,No,Thur,Lunch,2 151 | 7.51,2.0,Male,No,Thur,Lunch,2 152 | 14.07,2.5,Male,No,Sun,Dinner,2 153 | 13.13,2.0,Male,No,Sun,Dinner,2 154 | 17.26,2.74,Male,No,Sun,Dinner,3 155 | 24.55,2.0,Male,No,Sun,Dinner,4 156 | 19.77,2.0,Male,No,Sun,Dinner,4 157 | 29.85,5.14,Female,No,Sun,Dinner,5 158 | 48.17,5.0,Male,No,Sun,Dinner,6 159 | 25.0,3.75,Female,No,Sun,Dinner,4 160 | 13.39,2.61,Female,No,Sun,Dinner,2 161 | 16.49,2.0,Male,No,Sun,Dinner,4 162 | 21.5,3.5,Male,No,Sun,Dinner,4 163 | 12.66,2.5,Male,No,Sun,Dinner,2 164 | 16.21,2.0,Female,No,Sun,Dinner,3 165 | 13.81,2.0,Male,No,Sun,Dinner,2 166 | 17.51,3.0,Female,Yes,Sun,Dinner,2 167 | 24.52,3.48,Male,No,Sun,Dinner,3 168 | 20.76,2.24,Male,No,Sun,Dinner,2 169 | 31.71,4.5,Male,No,Sun,Dinner,4 170 | 10.59,1.61,Female,Yes,Sat,Dinner,2 171 | 10.63,2.0,Female,Yes,Sat,Dinner,2 172 | 50.81,10.0,Male,Yes,Sat,Dinner,3 173 | 15.81,3.16,Male,Yes,Sat,Dinner,2 174 | 7.25,5.15,Male,Yes,Sun,Dinner,2 175 | 31.85,3.18,Male,Yes,Sun,Dinner,2 176 | 16.82,4.0,Male,Yes,Sun,Dinner,2 177 | 32.9,3.11,Male,Yes,Sun,Dinner,2 178 | 17.89,2.0,Male,Yes,Sun,Dinner,2 179 | 14.48,2.0,Male,Yes,Sun,Dinner,2 180 | 9.6,4.0,Female,Yes,Sun,Dinner,2 181 | 34.63,3.55,Male,Yes,Sun,Dinner,2 182 | 34.65,3.68,Male,Yes,Sun,Dinner,4 183 | 23.33,5.65,Male,Yes,Sun,Dinner,2 184 | 45.35,3.5,Male,Yes,Sun,Dinner,3 185 | 23.17,6.5,Male,Yes,Sun,Dinner,4 186 | 40.55,3.0,Male,Yes,Sun,Dinner,2 187 | 20.69,5.0,Male,No,Sun,Dinner,5 188 | 20.9,3.5,Female,Yes,Sun,Dinner,3 189 | 30.46,2.0,Male,Yes,Sun,Dinner,5 190 | 18.15,3.5,Female,Yes,Sun,Dinner,3 191 | 23.1,4.0,Male,Yes,Sun,Dinner,3 192 | 15.69,1.5,Male,Yes,Sun,Dinner,2 193 | 19.81,4.19,Female,Yes,Thur,Lunch,2 194 | 28.44,2.56,Male,Yes,Thur,Lunch,2 195 | 15.48,2.02,Male,Yes,Thur,Lunch,2 196 | 16.58,4.0,Male,Yes,Thur,Lunch,2 197 | 7.56,1.44,Male,No,Thur,Lunch,2 198 | 10.34,2.0,Male,Yes,Thur,Lunch,2 199 | 43.11,5.0,Female,Yes,Thur,Lunch,4 200 | 13.0,2.0,Female,Yes,Thur,Lunch,2 201 | 13.51,2.0,Male,Yes,Thur,Lunch,2 202 | 18.71,4.0,Male,Yes,Thur,Lunch,3 203 | 12.74,2.01,Female,Yes,Thur,Lunch,2 204 | 13.0,2.0,Female,Yes,Thur,Lunch,2 205 | 16.4,2.5,Female,Yes,Thur,Lunch,2 206 | 20.53,4.0,Male,Yes,Thur,Lunch,4 207 | 16.47,3.23,Female,Yes,Thur,Lunch,3 208 | 26.59,3.41,Male,Yes,Sat,Dinner,3 209 | 38.73,3.0,Male,Yes,Sat,Dinner,4 210 | 24.27,2.03,Male,Yes,Sat,Dinner,2 211 | 12.76,2.23,Female,Yes,Sat,Dinner,2 212 | 30.06,2.0,Male,Yes,Sat,Dinner,3 213 | 25.89,5.16,Male,Yes,Sat,Dinner,4 214 | 48.33,9.0,Male,No,Sat,Dinner,4 215 | 13.27,2.5,Female,Yes,Sat,Dinner,2 216 | 28.17,6.5,Female,Yes,Sat,Dinner,3 217 | 12.9,1.1,Female,Yes,Sat,Dinner,2 218 | 28.15,3.0,Male,Yes,Sat,Dinner,5 219 | 11.59,1.5,Male,Yes,Sat,Dinner,2 220 | 7.74,1.44,Male,Yes,Sat,Dinner,2 221 | 30.14,3.09,Female,Yes,Sat,Dinner,4 222 | 12.16,2.2,Male,Yes,Fri,Lunch,2 223 | 13.42,3.48,Female,Yes,Fri,Lunch,2 224 | 8.58,1.92,Male,Yes,Fri,Lunch,1 225 | 15.98,3.0,Female,No,Fri,Lunch,3 226 | 13.42,1.58,Male,Yes,Fri,Lunch,2 227 | 16.27,2.5,Female,Yes,Fri,Lunch,2 228 | 10.09,2.0,Female,Yes,Fri,Lunch,2 229 | 20.45,3.0,Male,No,Sat,Dinner,4 230 | 13.28,2.72,Male,No,Sat,Dinner,2 231 | 22.12,2.88,Female,Yes,Sat,Dinner,2 232 | 24.01,2.0,Male,Yes,Sat,Dinner,4 233 | 15.69,3.0,Male,Yes,Sat,Dinner,3 234 | 11.61,3.39,Male,No,Sat,Dinner,2 235 | 10.77,1.47,Male,No,Sat,Dinner,2 236 | 15.53,3.0,Male,Yes,Sat,Dinner,2 237 | 10.07,1.25,Male,No,Sat,Dinner,2 238 | 12.6,1.0,Male,Yes,Sat,Dinner,2 239 | 32.83,1.17,Male,Yes,Sat,Dinner,2 240 | 35.83,4.67,Female,No,Sat,Dinner,3 241 | 29.03,5.92,Male,No,Sat,Dinner,3 242 | 27.18,2.0,Female,Yes,Sat,Dinner,2 243 | 22.67,2.0,Male,Yes,Sat,Dinner,2 244 | 17.82,1.75,Male,No,Sat,Dinner,2 245 | 18.78,3.0,Female,No,Thur,Dinner,2 246 | -------------------------------------------------------------------------------- /data/個股_類別.rar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Wei1234c/Introduction_to_Pandas/af523d3dc81a1bf49a03740781b1a918ace2cfc9/data/個股_類別.rar -------------------------------------------------------------------------------- /jpgs/MyPicture1.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Wei1234c/Introduction_to_Pandas/af523d3dc81a1bf49a03740781b1a918ace2cfc9/jpgs/MyPicture1.jpg -------------------------------------------------------------------------------- /notebooks/0. Pandas入門介紹.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": { 6 | "slideshow": { 7 | "slide_type": "slide" 8 | } 9 | }, 10 | "source": [ 11 | "# Pandas 入門介紹\n", 12 | "[Taichung.py](http://www.meetup.com/Taichung-Python-Meetup/) \n", 13 | "2016/3/12\n", 14 | "Wei Lin \n", 15 | "[Wei1234c@gmail.com](mailto://wei1234c@gmail.com) " 16 | ] 17 | }, 18 | { 19 | "cell_type": "markdown", 20 | "metadata": { 21 | "slideshow": { 22 | "slide_type": "slide" 23 | } 24 | }, 25 | "source": [ 26 | "## Books: \n", 27 | "[Python for Data Analysis](http://www.books.com.tw/products/F012771443) \n", 28 | "
\"HTML5
" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": { 34 | "collapsed": true, 35 | "slideshow": { 36 | "slide_type": "slide" 37 | } 38 | }, 39 | "source": [ 40 | "## Videos: \n", 41 | "[Data analysis in Python with pandas - Wes McKinney (Pandas之父)](https://www.youtube.com/watch?v=w26x-z-BdWQ) \n", 42 | "[Analyzing data with Pandas - PyCon SE 2015](https://www.youtube.com/watch?v=kSM8S76qYz0) \n", 43 | "[Pandas From The Ground Up - PyCon 2015](https://www.youtube.com/watch?v=5JnMutdy6Fw) / [Brandon’s Pandas Tutorial](https://github.com/brandon-rhodes/pycon-pandas-tutorial) \n", 44 | "[Hands-on Data Analysis with Python - PyCon 2015](https://www.youtube.com/watch?v=L4Hbv4ugUWk&list=PLHJdMADCPuGQeXXvSJnXGNhvYoOwcXjUD&index=4) " 45 | ] 46 | }, 47 | { 48 | "cell_type": "markdown", 49 | "metadata": { 50 | "slideshow": { 51 | "slide_type": "slide" 52 | } 53 | }, 54 | "source": [ 55 | "## Documentation:\n", 56 | "[pandas documentation — API Reference](http://pandas.pydata.org/pandas-docs/stable/api.html) " 57 | ] 58 | }, 59 | { 60 | "cell_type": "markdown", 61 | "metadata": { 62 | "slideshow": { 63 | "slide_type": "slide" 64 | } 65 | }, 66 | "source": [ 67 | "## About Me\n", 68 | "- Wei Lin : \n", 69 | " - [Wei1234c@gmail.com](mailto://Wei1234c@gmail.com)\n", 70 | " - [Twitter: @Wei_1144](https://twitter.com/Wei_1144)\n", 71 | " - [facebook: Wei Lin](https://www.facebook.com/wei.lin.921025)\n", 72 | " - [Github: wei1234c](https://github.com/Wei1234c)\n", 73 | "- A planner in private enterprises\n", 74 | "- Started learning Python in 2015\n", 75 | "- Interested in Data-Science and A.I.\n", 76 | "- [Speaker of PyCon TW 2016 - 2016/6/3](https://tw.pycon.org/2016/zh-hant/events/talk/68823578639859763/) \n", 77 | "- [Speaker of PyCon JP 2016 - 2016/9/22](https://pycon.jp/2016/en/schedule/presentation/5/)" 78 | ] 79 | }, 80 | { 81 | "cell_type": "markdown", 82 | "metadata": { 83 | "slideshow": { 84 | "slide_type": "slide" 85 | } 86 | }, 87 | "source": [ 88 | "## About this talk\n", 89 | "1. Pandas - main classes and structure\n", 90 | "2. Pandas - I/O tools\n", 91 | "3. Pandas - ETL tools\n", 92 | "4. Pandas - plotting\n", 93 | "5. Pandas - GroupBy" 94 | ] 95 | }, 96 | { 97 | "cell_type": "markdown", 98 | "metadata": { 99 | "slideshow": { 100 | "slide_type": "slide" 101 | } 102 | }, 103 | "source": [ 104 | "## Design of this talk\n", 105 | "1. Theory and Practice\n", 106 | "2. Learning a language\n", 107 | "3. What if ..." 108 | ] 109 | }, 110 | { 111 | "cell_type": "markdown", 112 | "metadata": { 113 | "slideshow": { 114 | "slide_type": "slide" 115 | } 116 | }, 117 | "source": [ 118 | "## [IPython Notebook](http://u.camdemy.com/media/399) 使用技巧簡介\n", 119 | "- Help\n", 120 | "- Insert a Cell\n", 121 | "- Menu / Document" 122 | ] 123 | } 124 | ], 125 | "metadata": { 126 | "anaconda-cloud": {}, 127 | "kernelspec": { 128 | "display_name": "Python [default]", 129 | "language": "python", 130 | "name": "python3" 131 | }, 132 | "language_info": { 133 | "codemirror_mode": { 134 | "name": "ipython", 135 | "version": 3 136 | }, 137 | "file_extension": ".py", 138 | "mimetype": "text/x-python", 139 | "name": "python", 140 | "nbconvert_exporter": "python", 141 | "pygments_lexer": "ipython3", 142 | "version": "3.5.1" 143 | } 144 | }, 145 | "nbformat": 4, 146 | "nbformat_minor": 0 147 | } 148 | -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/2. Pandas - IO tools.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 2. Pandas - IO tools" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 2, 13 | "metadata": { 14 | "collapsed": false 15 | }, 16 | "outputs": [ 17 | { 18 | "name": "stdout", 19 | "output_type": "stream", 20 | "text": [ 21 | "Using matplotlib backend: Qt4Agg\n", 22 | "Populating the interactive namespace from numpy and matplotlib\n" 23 | ] 24 | } 25 | ], 26 | "source": [ 27 | "%pylab\n", 28 | "from pandas import Series, DataFrame\n", 29 | "import pandas as pd" 30 | ] 31 | }, 32 | { 33 | "cell_type": "markdown", 34 | "metadata": {}, 35 | "source": [ 36 | "## 讀寫本文格式的數據" 37 | ] 38 | }, 39 | { 40 | "cell_type": "markdown", 41 | "metadata": { 42 | "collapsed": true 43 | }, 44 | "source": [ 45 | "將text轉換為DataFrame的函數，其選項分為:\n", 46 | "- 索引\n", 47 | "- 類型推斷 和 數據轉換\n", 48 | "- 日期解析\n", 49 | "- 佚代\n", 50 | "- 不規整數據問題" 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "類型推斷(type inference)是最重要的功能之一，不需要指定列的資料型態" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 3, 63 | "metadata": { 64 | "collapsed": false 65 | }, 66 | "outputs": [ 67 | { 68 | "name": "stdout", 69 | "output_type": "stream", 70 | "text": [ 71 | "a,b,c,d,message\n", 72 | "1,2,3,4,hello\n", 73 | "5,6,7,8,world\n", 74 | "9,10,11,12,foo\n" 75 | ] 76 | } 77 | ], 78 | "source": [ 79 | "!cat ex1.csv" 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": 4, 85 | "metadata": { 86 | "collapsed": false 87 | }, 88 | "outputs": [ 89 | { 90 | "name": "stdout", 91 | "output_type": "stream", 92 | "text": [ 93 | "a,b,c,d,message\n", 94 | "1,2,3,4,hello\n", 95 | "5,6,7,8,world\n", 96 | "9,10,11,12,foo\n" 97 | ] 98 | } 99 | ], 100 | "source": [ 101 | "!type ex1.csv" 102 | ] 103 | }, 104 | { 105 | "cell_type": "code", 106 | "execution_count": 5, 107 | "metadata": { 108 | "collapsed": false 109 | }, 110 | "outputs": [ 111 | { 112 | "data": { 113 | "text/html": [ 114 | "
\n", 115 | "\n", 116 | " \n", 117 | " \n", 118 | " \n", 119 | " \n", 120 | " \n", 121 | " \n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | "
abcdmessage
01234hello
15678world
29101112foo
\n", 153 | "
" 154 | ], 155 | "text/plain": [ 156 | " a b c d message\n", 157 | "0 1 2 3 4 hello\n", 158 | "1 5 6 7 8 world\n", 159 | "2 9 10 11 12 foo" 160 | ] 161 | }, 162 | "execution_count": 5, 163 | "metadata": {}, 164 | "output_type": "execute_result" 165 | } 166 | ], 167 | "source": [ 168 | "# read_csv 讀入 csv檔案\n", 169 | "df = pd.read_csv('ex1.csv')\n", 170 | "df" 171 | ] 172 | }, 173 | { 174 | "cell_type": "code", 175 | "execution_count": 6, 176 | "metadata": { 177 | "collapsed": false 178 | }, 179 | "outputs": [ 180 | { 181 | "data": { 182 | "text/html": [ 183 | "
\n", 184 | "\n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | "
abcdmessage
01234hello
15678world
29101112foo
\n", 222 | "
" 223 | ], 224 | "text/plain": [ 225 | " a b c d message\n", 226 | "0 1 2 3 4 hello\n", 227 | "1 5 6 7 8 world\n", 228 | "2 9 10 11 12 foo" 229 | ] 230 | }, 231 | "execution_count": 6, 232 | "metadata": {}, 233 | "output_type": "execute_result" 234 | } 235 | ], 236 | "source": [ 237 | "# 也可以讀入table，不過需要指定分隔符號\n", 238 | "df = pd.read_table('ex1.csv', sep = ',')\n", 239 | "df" 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "execution_count": 7, 245 | "metadata": { 246 | "collapsed": false 247 | }, 248 | "outputs": [ 249 | { 250 | "name": "stdout", 251 | "output_type": "stream", 252 | "text": [ 253 | "1,2,3,4,hello\n", 254 | "5,6,7,8,world\n", 255 | "9,10,11,12,foo\n" 256 | ] 257 | } 258 | ], 259 | "source": [ 260 | "# 沒有欄位名稱列的檔案\n", 261 | "!type ex2.csv" 262 | ] 263 | }, 264 | { 265 | "cell_type": "code", 266 | "execution_count": 8, 267 | "metadata": { 268 | "collapsed": false 269 | }, 270 | "outputs": [ 271 | { 272 | "data": { 273 | "text/html": [ 274 | "
\n", 275 | "\n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | "
1234hello
05678world
19101112foo
\n", 305 | "
" 306 | ], 307 | "text/plain": [ 308 | " 1 2 3 4 hello\n", 309 | "0 5 6 7 8 world\n", 310 | "1 9 10 11 12 foo" 311 | ] 312 | }, 313 | "execution_count": 8, 314 | "metadata": {}, 315 | "output_type": "execute_result" 316 | } 317 | ], 318 | "source": [ 319 | "# 預設會把第一列當作 欄位名稱列\n", 320 | "df = pd.read_csv('ex2.csv', )\n", 321 | "df" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": 9, 327 | "metadata": { 328 | "collapsed": false 329 | }, 330 | "outputs": [ 331 | { 332 | "data": { 333 | "text/html": [ 334 | "
\n", 335 | "\n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | "
01234
01234hello
15678world
29101112foo
\n", 373 | "
" 374 | ], 375 | "text/plain": [ 376 | " 0 1 2 3 4\n", 377 | "0 1 2 3 4 hello\n", 378 | "1 5 6 7 8 world\n", 379 | "2 9 10 11 12 foo" 380 | ] 381 | }, 382 | "execution_count": 9, 383 | "metadata": {}, 384 | "output_type": "execute_result" 385 | } 386 | ], 387 | "source": [ 388 | "# 標示沒有欄位名稱列\n", 389 | "df = pd.read_csv('ex2.csv', header = None)\n", 390 | "df" 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": 10, 396 | "metadata": { 397 | "collapsed": false 398 | }, 399 | "outputs": [ 400 | { 401 | "data": { 402 | "text/html": [ 403 | "
\n", 404 | "\n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | "
abcdmessage
01234hello
15678world
29101112foo
\n", 442 | "
" 443 | ], 444 | "text/plain": [ 445 | " a b c d message\n", 446 | "0 1 2 3 4 hello\n", 447 | "1 5 6 7 8 world\n", 448 | "2 9 10 11 12 foo" 449 | ] 450 | }, 451 | "execution_count": 10, 452 | "metadata": {}, 453 | "output_type": "execute_result" 454 | } 455 | ], 456 | "source": [ 457 | "# 自定義 欄位名稱\n", 458 | "fields = ['a', 'b', 'c', 'd', 'message']\n", 459 | "df = pd.read_csv('ex2.csv', names = fields)\n", 460 | "df" 461 | ] 462 | }, 463 | { 464 | "cell_type": "code", 465 | "execution_count": 11, 466 | "metadata": { 467 | "collapsed": false 468 | }, 469 | "outputs": [ 470 | { 471 | "data": { 472 | "text/html": [ 473 | "
\n", 474 | "\n", 475 | " \n", 476 | " \n", 477 | " \n", 478 | " \n", 479 | " \n", 480 | " \n", 481 | " \n", 482 | " \n", 483 | " \n", 484 | " \n", 485 | " \n", 486 | " \n", 487 | " \n", 488 | " \n", 489 | " \n", 490 | " \n", 491 | " \n", 492 | " \n", 493 | " \n", 494 | " \n", 495 | " \n", 496 | " \n", 497 | " \n", 498 | " \n", 499 | " \n", 500 | " \n", 501 | " \n", 502 | " \n", 503 | " \n", 504 | " \n", 505 | " \n", 506 | " \n", 507 | " \n", 508 | " \n", 509 | " \n", 510 | " \n", 511 | " \n", 512 | " \n", 513 | " \n", 514 | "
abcd
message
hello1234
world5678
foo9101112
\n", 515 | "
" 516 | ], 517 | "text/plain": [ 518 | " a b c d\n", 519 | "message \n", 520 | "hello 1 2 3 4\n", 521 | "world 5 6 7 8\n", 522 | "foo 9 10 11 12" 523 | ] 524 | }, 525 | "execution_count": 11, 526 | "metadata": {}, 527 | "output_type": "execute_result" 528 | } 529 | ], 530 | "source": [ 531 | "# 可以 使用 index_col 參數，將某一欄設定為DataFrame的索引\n", 532 | "fields = ['a', 'b', 'c', 'd', 'message']\n", 533 | "df = pd.read_csv('ex2.csv', names = fields, index_col = 'message')\n", 534 | "df" 535 | ] 536 | }, 537 | { 538 | "cell_type": "code", 539 | "execution_count": 12, 540 | "metadata": { 541 | "collapsed": false 542 | }, 543 | "outputs": [ 544 | { 545 | "name": "stdout", 546 | "output_type": "stream", 547 | "text": [ 548 | "key1,key2,value1,value2\n", 549 | "one,a,1,2\n", 550 | "one,b,3,4\n", 551 | "one,c,5,6\n", 552 | "one,d,7,8\n", 553 | "two,a,9,10\n", 554 | "two,b,11,12\n", 555 | "two,c,13,14\n", 556 | "two,d,15,16\n" 557 | ] 558 | }, 559 | { 560 | "data": { 561 | "text/html": [ 562 | "
\n", 563 | "\n", 564 | " \n", 565 | " \n", 566 | " \n", 567 | " \n", 568 | " \n", 569 | " \n", 570 | " \n", 571 | " \n", 572 | " \n", 573 | " \n", 574 | " \n", 575 | " \n", 576 | " \n", 577 | " \n", 578 | " \n", 579 | " \n", 580 | " \n", 581 | " \n", 582 | " \n", 583 | " \n", 584 | " \n", 585 | " \n", 586 | " \n", 587 | " \n", 588 | " \n", 589 | " \n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | "
value1value2
key1key2
onea12
b34
c56
d78
twoa910
b1112
c1314
d1516
\n", 623 | "
" 624 | ], 625 | "text/plain": [ 626 | " value1 value2\n", 627 | "key1 key2 \n", 628 | "one a 1 2\n", 629 | " b 3 4\n", 630 | " c 5 6\n", 631 | " d 7 8\n", 632 | "two a 9 10\n", 633 | " b 11 12\n", 634 | " c 13 14\n", 635 | " d 15 16" 636 | ] 637 | }, 638 | "execution_count": 12, 639 | "metadata": {}, 640 | "output_type": "execute_result" 641 | } 642 | ], 643 | "source": [ 644 | "# 可以 使用 index_col 參數，將多個欄設定為DataFrame的層次化索引 \n", 645 | "!type ex3.csv\n", 646 | "df = pd.read_csv('ex3.csv', index_col = ['key1', 'key2'])\n", 647 | "df" 648 | ] 649 | }, 650 | { 651 | "cell_type": "code", 652 | "execution_count": 13, 653 | "metadata": { 654 | "collapsed": false 655 | }, 656 | "outputs": [ 657 | { 658 | "name": "stdout", 659 | "output_type": "stream", 660 | "text": [ 661 | "\tA\tB\tC\n", 662 | "aaa \t-0.264 \t-1.026 \t-0.619\n", 663 | "bbb\t 0.927\t 0.302\t -0.032\n", 664 | "ccc -0.265\t -0.385\t -0.217\n", 665 | "\t\n" 666 | ] 667 | }, 668 | { 669 | "data": { 670 | "text/html": [ 671 | "
\n", 672 | "\n", 673 | " \n", 674 | " \n", 675 | " \n", 676 | " \n", 677 | " \n", 678 | " \n", 679 | " \n", 680 | " \n", 681 | " \n", 682 | " \n", 683 | " \n", 684 | " \n", 685 | " \n", 686 | " \n", 687 | " \n", 688 | " \n", 689 | " \n", 690 | " \n", 691 | " \n", 692 | " \n", 693 | " \n", 694 | " \n", 695 | " \n", 696 | " \n", 697 | " \n", 698 | " \n", 699 | " \n", 700 | " \n", 701 | "
ABC
aaa-0.264-1.026-0.619
bbb0.9270.302-0.032
ccc-0.265-0.385-0.217
\n", 702 | "
" 703 | ], 704 | "text/plain": [ 705 | " A B C\n", 706 | "aaa -0.264 -1.026 -0.619\n", 707 | "bbb 0.927 0.302 -0.032\n", 708 | "ccc -0.265 -0.385 -0.217" 709 | ] 710 | }, 711 | "execution_count": 13, 712 | "metadata": {}, 713 | "output_type": "execute_result" 714 | } 715 | ], 716 | "source": [ 717 | "# 如果不是以固定的分隔符號來分隔字段，可以用 read_table + regex 作為 sep參數\n", 718 | "# 由於列名比資料列的數量少，因此read_table推斷第一列應該是DataFrame的索引\n", 719 | "# 以不定數量的空白做分隔\n", 720 | "!type \"ex3 - 1.csv\" \n", 721 | "df = pd.read_table('ex3 - 1.csv', sep = '\\s+')\n", 722 | "df" 723 | ] 724 | }, 725 | { 726 | "cell_type": "code", 727 | "execution_count": 14, 728 | "metadata": { 729 | "collapsed": false 730 | }, 731 | "outputs": [ 732 | { 733 | "name": "stdout", 734 | "output_type": "stream", 735 | "text": [ 736 | "# hey!\n", 737 | "a,b,c,d,message\n", 738 | "# just wanted to make things more difficult for you\n", 739 | "# who read CSV files with computers, anyway?\n", 740 | "1,2,3,4,hello\n", 741 | "5,6,7,8,world\n", 742 | "9,10,11,12,foo\n" 743 | ] 744 | }, 745 | { 746 | "data": { 747 | "text/html": [ 748 | "
\n", 749 | "\n", 750 | " \n", 751 | " \n", 752 | " \n", 753 | " \n", 754 | " \n", 755 | " \n", 756 | " \n", 757 | " \n", 758 | " \n", 759 | " \n", 760 | " \n", 761 | " \n", 762 | " \n", 763 | " \n", 764 | " \n", 765 | " \n", 766 | " \n", 767 | " \n", 768 | " \n", 769 | " \n", 770 | " \n", 771 | " \n", 772 | " \n", 773 | " \n", 774 | " \n", 775 | " \n", 776 | " \n", 777 | " \n", 778 | " \n", 779 | " \n", 780 | " \n", 781 | " \n", 782 | " \n", 783 | " \n", 784 | " \n", 785 | " \n", 786 | " \n", 787 | " \n", 788 | " \n", 789 | "
abcd
message
hello1234
world5678
foo9101112
\n", 790 | "
" 791 | ], 792 | "text/plain": [ 793 | " a b c d\n", 794 | "message \n", 795 | "hello 1 2 3 4\n", 796 | "world 5 6 7 8\n", 797 | "foo 9 10 11 12" 798 | ] 799 | }, 800 | "execution_count": 14, 801 | "metadata": {}, 802 | "output_type": "execute_result" 803 | } 804 | ], 805 | "source": [ 806 | "# 讀檔時，可以用 skiprows 來跳過指定的 rows\n", 807 | "!type ex4.csv\n", 808 | "df = pd.read_csv('ex4.csv', skiprows = [0, 2, 3], index_col = 'message')\n", 809 | "df" 810 | ] 811 | }, 812 | { 813 | "cell_type": "code", 814 | "execution_count": 15, 815 | "metadata": { 816 | "collapsed": false 817 | }, 818 | "outputs": [ 819 | { 820 | "name": "stdout", 821 | "output_type": "stream", 822 | "text": [ 823 | "something,a,b,c,d,message\n", 824 | "one,1,2,3,4,NA\n", 825 | "two,5,6,,8,world\n", 826 | "three,9,10,11,12,foo \n" 827 | ] 828 | }, 829 | { 830 | "data": { 831 | "text/html": [ 832 | "
\n", 833 | "\n", 834 | " \n", 835 | " \n", 836 | " \n", 837 | " \n", 838 | " \n", 839 | " \n", 840 | " \n", 841 | " \n", 842 | " \n", 843 | " \n", 844 | " \n", 845 | " \n", 846 | " \n", 847 | " \n", 848 | " \n", 849 | " \n", 850 | " \n", 851 | " \n", 852 | " \n", 853 | " \n", 854 | " \n", 855 | " \n", 856 | " \n", 857 | " \n", 858 | " \n", 859 | " \n", 860 | " \n", 861 | " \n", 862 | " \n", 863 | " \n", 864 | " \n", 865 | " \n", 866 | " \n", 867 | " \n", 868 | " \n", 869 | " \n", 870 | " \n", 871 | " \n", 872 | " \n", 873 | " \n", 874 | " \n", 875 | " \n", 876 | " \n", 877 | " \n", 878 | "
abcdmessage
something
one123.04NaN
two56NaN8world
three91011.012foo
\n", 879 | "
" 880 | ], 881 | "text/plain": [ 882 | " a b c d message\n", 883 | "something \n", 884 | "one 1 2 3.0 4 NaN\n", 885 | "two 5 6 NaN 8 world\n", 886 | "three 9 10 11.0 12 foo " 887 | ] 888 | }, 889 | "execution_count": 15, 890 | "metadata": {}, 891 | "output_type": "execute_result" 892 | } 893 | ], 894 | "source": [ 895 | "# 缺失數據的處理\n", 896 | "# read_csv 會自動判斷，然後以NaN標示缺失數據的位置\n", 897 | "!type ex5.csv\n", 898 | "df = pd.read_csv('ex5.csv', index_col = 'something')\n", 899 | "df" 900 | ] 901 | }, 902 | { 903 | "cell_type": "code", 904 | "execution_count": 16, 905 | "metadata": { 906 | "collapsed": false 907 | }, 908 | "outputs": [ 909 | { 910 | "data": { 911 | "text/html": [ 912 | "
\n", 913 | "\n", 914 | " \n", 915 | " \n", 916 | " \n", 917 | " \n", 918 | " \n", 919 | " \n", 920 | " \n", 921 | " \n", 922 | " \n", 923 | " \n", 924 | " \n", 925 | " \n", 926 | " \n", 927 | " \n", 928 | " \n", 929 | " \n", 930 | " \n", 931 | " \n", 932 | " \n", 933 | " \n", 934 | " \n", 935 | " \n", 936 | " \n", 937 | " \n", 938 | " \n", 939 | " \n", 940 | " \n", 941 | " \n", 942 | " \n", 943 | " \n", 944 | " \n", 945 | " \n", 946 | " \n", 947 | " \n", 948 | " \n", 949 | " \n", 950 | " \n", 951 | " \n", 952 | " \n", 953 | " \n", 954 | " \n", 955 | " \n", 956 | " \n", 957 | " \n", 958 | "
abcdmessage
something
oneFalseFalseFalseFalseTrue
twoFalseFalseTrueFalseFalse
threeFalseFalseFalseFalseFalse
\n", 959 | "
" 960 | ], 961 | "text/plain": [ 962 | " a b c d message\n", 963 | "something \n", 964 | "one False False False False True\n", 965 | "two False False True False False\n", 966 | "three False False False False False" 967 | ] 968 | }, 969 | "execution_count": 16, 970 | "metadata": {}, 971 | "output_type": "execute_result" 972 | } 973 | ], 974 | "source": [ 975 | "# isnull()，判斷元素是否為NaN\n", 976 | "df.isnull()" 977 | ] 978 | }, 979 | { 980 | "cell_type": "code", 981 | "execution_count": 17, 982 | "metadata": { 983 | "collapsed": false 984 | }, 985 | "outputs": [ 986 | { 987 | "data": { 988 | "text/html": [ 989 | "
\n", 990 | "\n", 991 | " \n", 992 | " \n", 993 | " \n", 994 | " \n", 995 | " \n", 996 | " \n", 997 | " \n", 998 | " \n", 999 | " \n", 1000 | " \n", 1001 | " \n", 1002 | " \n", 1003 | " \n", 1004 | " \n", 1005 | " \n", 1006 | " \n", 1007 | " \n", 1008 | " \n", 1009 | " \n", 1010 | " \n", 1011 | " \n", 1012 | " \n", 1013 | " \n", 1014 | " \n", 1015 | " \n", 1016 | " \n", 1017 | " \n", 1018 | " \n", 1019 | " \n", 1020 | " \n", 1021 | " \n", 1022 | " \n", 1023 | " \n", 1024 | " \n", 1025 | " \n", 1026 | " \n", 1027 | " \n", 1028 | " \n", 1029 | " \n", 1030 | " \n", 1031 | " \n", 1032 | " \n", 1033 | " \n", 1034 | " \n", 1035 | "
abcdmessage
something
oneFalseFalseFalseFalseTrue
twoFalseFalseTrueFalseFalse
threeFalseFalseFalseFalseFalse
\n", 1036 | "
" 1037 | ], 1038 | "text/plain": [ 1039 | " a b c d message\n", 1040 | "something \n", 1041 | "one False False False False True\n", 1042 | "two False False True False False\n", 1043 | "three False False False False False" 1044 | ] 1045 | }, 1046 | "execution_count": 17, 1047 | "metadata": {}, 1048 | "output_type": "execute_result" 1049 | } 1050 | ], 1051 | "source": [ 1052 | "pd.isnull(df)" 1053 | ] 1054 | }, 1055 | { 1056 | "cell_type": "code", 1057 | "execution_count": 18, 1058 | "metadata": { 1059 | "collapsed": false 1060 | }, 1061 | "outputs": [ 1062 | { 1063 | "data": { 1064 | "text/html": [ 1065 | "
\n", 1066 | "\n", 1067 | " \n", 1068 | " \n", 1069 | " \n", 1070 | " \n", 1071 | " \n", 1072 | " \n", 1073 | " \n", 1074 | " \n", 1075 | " \n", 1076 | " \n", 1077 | " \n", 1078 | " \n", 1079 | " \n", 1080 | " \n", 1081 | " \n", 1082 | " \n", 1083 | " \n", 1084 | " \n", 1085 | " \n", 1086 | " \n", 1087 | " \n", 1088 | " \n", 1089 | " \n", 1090 | " \n", 1091 | " \n", 1092 | " \n", 1093 | " \n", 1094 | " \n", 1095 | " \n", 1096 | " \n", 1097 | " \n", 1098 | " \n", 1099 | " \n", 1100 | " \n", 1101 | " \n", 1102 | " \n", 1103 | " \n", 1104 | " \n", 1105 | " \n", 1106 | " \n", 1107 | " \n", 1108 | " \n", 1109 | " \n", 1110 | " \n", 1111 | "
abcdmessage
something
one123.04NaN
two56NaN8world
three91011.012foo
\n", 1112 | "
" 1113 | ], 1114 | "text/plain": [ 1115 | " a b c d message\n", 1116 | "something \n", 1117 | "one 1 2 3.0 4 NaN\n", 1118 | "two 5 6 NaN 8 world\n", 1119 | "three 9 10 11.0 12 foo " 1120 | ] 1121 | }, 1122 | "execution_count": 18, 1123 | "metadata": {}, 1124 | "output_type": "execute_result" 1125 | } 1126 | ], 1127 | "source": [ 1128 | "# na_values 參數可指定用於標示缺失數據的字串\n", 1129 | "df = pd.read_csv('ex5.csv', index_col = 'something', na_values = ['NULL'])\n", 1130 | "df" 1131 | ] 1132 | }, 1133 | { 1134 | "cell_type": "code", 1135 | "execution_count": 19, 1136 | "metadata": { 1137 | "collapsed": false 1138 | }, 1139 | "outputs": [ 1140 | { 1141 | "data": { 1142 | "text/html": [ 1143 | "
\n", 1144 | "\n", 1145 | " \n", 1146 | " \n", 1147 | " \n", 1148 | " \n", 1149 | " \n", 1150 | " \n", 1151 | " \n", 1152 | " \n", 1153 | " \n", 1154 | " \n", 1155 | " \n", 1156 | " \n", 1157 | " \n", 1158 | " \n", 1159 | " \n", 1160 | " \n", 1161 | " \n", 1162 | " \n", 1163 | " \n", 1164 | " \n", 1165 | " \n", 1166 | " \n", 1167 | " \n", 1168 | " \n", 1169 | " \n", 1170 | " \n", 1171 | " \n", 1172 | " \n", 1173 | " \n", 1174 | " \n", 1175 | " \n", 1176 | " \n", 1177 | " \n", 1178 | " \n", 1179 | " \n", 1180 | " \n", 1181 | " \n", 1182 | " \n", 1183 | " \n", 1184 | " \n", 1185 | "
somethingabcdmessage
0one123.04NaN
1NaN56NaN8world
2three91011.012foo
\n", 1186 | "
" 1187 | ], 1188 | "text/plain": [ 1189 | " something a b c d message\n", 1190 | "0 one 1 2 3.0 4 NaN\n", 1191 | "1 NaN 5 6 NaN 8 world\n", 1192 | "2 three 9 10 11.0 12 foo " 1193 | ] 1194 | }, 1195 | "execution_count": 19, 1196 | "metadata": {}, 1197 | "output_type": "execute_result" 1198 | } 1199 | ], 1200 | "source": [ 1201 | "# 為各列分別指定不同的 缺失值標示字串\n", 1202 | "sentinels = {'message': ['foo', 'NA'], 'something': ['two']}\n", 1203 | "df = pd.read_csv('ex5.csv', na_values = sentinels)\n", 1204 | "df" 1205 | ] 1206 | }, 1207 | { 1208 | "cell_type": "markdown", 1209 | "metadata": {}, 1210 | "source": [ 1211 | "### 逐塊讀取文本文件" 1212 | ] 1213 | }, 1214 | { 1215 | "cell_type": "code", 1216 | "execution_count": 20, 1217 | "metadata": { 1218 | "collapsed": false 1219 | }, 1220 | "outputs": [ 1221 | { 1222 | "name": "stdout", 1223 | "output_type": "stream", 1224 | "text": [ 1225 | "something,a,b,c,d,message\n", 1226 | "one,1,2,3,4,NA\n", 1227 | "two,5,6,,8,world\n", 1228 | "three,9,10,11,12,foo \n" 1229 | ] 1230 | }, 1231 | { 1232 | "data": { 1233 | "text/html": [ 1234 | "
\n", 1235 | "\n", 1236 | " \n", 1237 | " \n", 1238 | " \n", 1239 | " \n", 1240 | " \n", 1241 | " \n", 1242 | " \n", 1243 | " \n", 1244 | " \n", 1245 | " \n", 1246 | " \n", 1247 | " \n", 1248 | " \n", 1249 | " \n", 1250 | " \n", 1251 | " \n", 1252 | " \n", 1253 | " \n", 1254 | " \n", 1255 | " \n", 1256 | " \n", 1257 | " \n", 1258 | " \n", 1259 | " \n", 1260 | " \n", 1261 | " \n", 1262 | " \n", 1263 | " \n", 1264 | " \n", 1265 | " \n", 1266 | " \n", 1267 | "
somethingabcdmessage
0one123.04NaN
1two56NaN8world
\n", 1268 | "
" 1269 | ], 1270 | "text/plain": [ 1271 | " something a b c d message\n", 1272 | "0 one 1 2 3.0 4 NaN\n", 1273 | "1 two 5 6 NaN 8 world" 1274 | ] 1275 | }, 1276 | "execution_count": 20, 1277 | "metadata": {}, 1278 | "output_type": "execute_result" 1279 | } 1280 | ], 1281 | "source": [ 1282 | "# 設定 nrows參數，設定讀入的列數\n", 1283 | "!type ex5.csv\n", 1284 | "df = pd.read_csv('ex5.csv', nrows = 2)\n", 1285 | "df" 1286 | ] 1287 | }, 1288 | { 1289 | "cell_type": "code", 1290 | "execution_count": 21, 1291 | "metadata": { 1292 | "collapsed": false 1293 | }, 1294 | "outputs": [ 1295 | { 1296 | "name": "stdout", 1297 | "output_type": "stream", 1298 | "text": [ 1299 | "something,a,b,c,d,message\n", 1300 | "one,1,2,3,4,NA\n", 1301 | "two,5,6,,8,world\n", 1302 | "three,9,10,11,12,foo \n" 1303 | ] 1304 | }, 1305 | { 1306 | "data": { 1307 | "text/plain": [ 1308 | "" 1309 | ] 1310 | }, 1311 | "execution_count": 21, 1312 | "metadata": {}, 1313 | "output_type": "execute_result" 1314 | } 1315 | ], 1316 | "source": [ 1317 | "# 如果要逐塊讀取，則設定chunksize\n", 1318 | "!type ex5.csv\n", 1319 | "chunker = pd.read_csv('ex5.csv', chunksize = 2)\n", 1320 | "chunker" 1321 | ] 1322 | }, 1323 | { 1324 | "cell_type": "code", 1325 | "execution_count": 22, 1326 | "metadata": { 1327 | "collapsed": false 1328 | }, 1329 | "outputs": [ 1330 | { 1331 | "data": { 1332 | "text/plain": [ 1333 | "two 1.0\n", 1334 | "three 1.0\n", 1335 | "one 1.0\n", 1336 | "dtype: float64" 1337 | ] 1338 | }, 1339 | "execution_count": 22, 1340 | "metadata": {}, 1341 | "output_type": "execute_result" 1342 | } 1343 | ], 1344 | "source": [ 1345 | "tot = Series([])\n", 1346 | "for piece in chunker:\n", 1347 | " tot = tot.add(piece['something'].value_counts(), fill_value = 0)\n", 1348 | "tot = tot.sort_values(ascending = False)\n", 1349 | "tot" 1350 | ] 1351 | }, 1352 | { 1353 | "cell_type": "markdown", 1354 | "metadata": {}, 1355 | "source": [ 1356 | "## 將數據寫出到文本格式" 1357 | ] 1358 | }, 1359 | { 1360 | "cell_type": "code", 1361 | "execution_count": 23, 1362 | "metadata": { 1363 | "collapsed": false 1364 | }, 1365 | "outputs": [ 1366 | { 1367 | "name": "stdout", 1368 | "output_type": "stream", 1369 | "text": [ 1370 | "something,a,b,c,d,message\n", 1371 | "one,1,2,3,4,NA\n", 1372 | "two,5,6,,8,world\n", 1373 | "three,9,10,11,12,foo \n" 1374 | ] 1375 | }, 1376 | { 1377 | "data": { 1378 | "text/html": [ 1379 | "
\n", 1380 | "\n", 1381 | " \n", 1382 | " \n", 1383 | " \n", 1384 | " \n", 1385 | " \n", 1386 | " \n", 1387 | " \n", 1388 | " \n", 1389 | " \n", 1390 | " \n", 1391 | " \n", 1392 | " \n", 1393 | " \n", 1394 | " \n", 1395 | " \n", 1396 | " \n", 1397 | " \n", 1398 | " \n", 1399 | " \n", 1400 | " \n", 1401 | " \n", 1402 | " \n", 1403 | " \n", 1404 | " \n", 1405 | " \n", 1406 | " \n", 1407 | " \n", 1408 | " \n", 1409 | " \n", 1410 | " \n", 1411 | " \n", 1412 | " \n", 1413 | " \n", 1414 | " \n", 1415 | " \n", 1416 | " \n", 1417 | " \n", 1418 | " \n", 1419 | " \n", 1420 | " \n", 1421 | "
somethingabcdmessage
0one123.04NaN
1two56NaN8world
2three91011.012foo
\n", 1422 | "
" 1423 | ], 1424 | "text/plain": [ 1425 | " something a b c d message\n", 1426 | "0 one 1 2 3.0 4 NaN\n", 1427 | "1 two 5 6 NaN 8 world\n", 1428 | "2 three 9 10 11.0 12 foo " 1429 | ] 1430 | }, 1431 | "execution_count": 23, 1432 | "metadata": {}, 1433 | "output_type": "execute_result" 1434 | } 1435 | ], 1436 | "source": [ 1437 | "!type ex5.csv\n", 1438 | "df = pd.read_csv('ex5.csv')\n", 1439 | "df" 1440 | ] 1441 | }, 1442 | { 1443 | "cell_type": "code", 1444 | "execution_count": 24, 1445 | "metadata": { 1446 | "collapsed": false 1447 | }, 1448 | "outputs": [ 1449 | { 1450 | "name": "stdout", 1451 | "output_type": "stream", 1452 | "text": [ 1453 | ",something,a,b,c,d,message\n", 1454 | "0,one,1,2,3.0,4,\n", 1455 | "1,two,5,6,,8,world\n", 1456 | "2,three,9,10,11.0,12,foo \n" 1457 | ] 1458 | } 1459 | ], 1460 | "source": [ 1461 | "# 以 to_csv() 將數據寫出到一個 以逗號分隔 的檔案中\n", 1462 | "df.to_csv('ex5-1.csv')\n", 1463 | "!type \"ex5-1.csv\"" 1464 | ] 1465 | }, 1466 | { 1467 | "cell_type": "code", 1468 | "execution_count": 25, 1469 | "metadata": { 1470 | "collapsed": false 1471 | }, 1472 | "outputs": [ 1473 | { 1474 | "name": "stdout", 1475 | "output_type": "stream", 1476 | "text": [ 1477 | "|something|a|b|c|d|message\n", 1478 | "0|one|1|2|3.0|4|\n", 1479 | "1|two|5|6||8|world\n", 1480 | "2|three|9|10|11.0|12|foo \n" 1481 | ] 1482 | } 1483 | ], 1484 | "source": [ 1485 | "# 寫出的時候，可以設定 sep 參數 指定其他的分隔符號\n", 1486 | "df.to_csv('ex5-1.csv', sep = '|')\n", 1487 | "!type \"ex5-1.csv\"" 1488 | ] 1489 | }, 1490 | { 1491 | "cell_type": "code", 1492 | "execution_count": 26, 1493 | "metadata": { 1494 | "collapsed": false 1495 | }, 1496 | "outputs": [ 1497 | { 1498 | "name": "stdout", 1499 | "output_type": "stream", 1500 | "text": [ 1501 | ",something,a,b,c,d,message\n", 1502 | "0,one,1,2,3.0,4,NULL\n", 1503 | "1,two,5,6,NULL,8,world\n", 1504 | "2,three,9,10,11.0,12,foo \n" 1505 | ] 1506 | } 1507 | ], 1508 | "source": [ 1509 | "# 設定 na_rep 參數，以其他的符號 明確地標示 缺失值\n", 1510 | "df.to_csv('ex5-1.csv', na_rep = 'NULL')\n", 1511 | "!type \"ex5-1.csv\"" 1512 | ] 1513 | }, 1514 | { 1515 | "cell_type": "code", 1516 | "execution_count": 27, 1517 | "metadata": { 1518 | "collapsed": false 1519 | }, 1520 | "outputs": [ 1521 | { 1522 | "name": "stdout", 1523 | "output_type": "stream", 1524 | "text": [ 1525 | "one,1,2,3.0,4,NULL\n", 1526 | "two,5,6,NULL,8,world\n", 1527 | "three,9,10,11.0,12,foo \n" 1528 | ] 1529 | } 1530 | ], 1531 | "source": [ 1532 | "# 可以禁止列出 row, column的標籤\n", 1533 | "# 不輸出index、header\n", 1534 | "df.to_csv('ex5-1.csv', na_rep = 'NULL', index = False, header = False) \n", 1535 | "!type \"ex5-1.csv\"" 1536 | ] 1537 | }, 1538 | { 1539 | "cell_type": "code", 1540 | "execution_count": 28, 1541 | "metadata": { 1542 | "collapsed": false 1543 | }, 1544 | "outputs": [ 1545 | { 1546 | "name": "stdout", 1547 | "output_type": "stream", 1548 | "text": [ 1549 | "something,a,b,c,d,message\n", 1550 | "one,1,2,3.0,4,NULL\n", 1551 | "two,5,6,NULL,8,world\n", 1552 | "three,9,10,11.0,12,foo \n" 1553 | ] 1554 | } 1555 | ], 1556 | "source": [ 1557 | "# 不輸出index\n", 1558 | "df.to_csv('ex5-1.csv', na_rep = 'NULL', index = False) \n", 1559 | "!type \"ex5-1.csv\"" 1560 | ] 1561 | }, 1562 | { 1563 | "cell_type": "code", 1564 | "execution_count": 29, 1565 | "metadata": { 1566 | "collapsed": false 1567 | }, 1568 | "outputs": [ 1569 | { 1570 | "name": "stdout", 1571 | "output_type": "stream", 1572 | "text": [ 1573 | "something,a,b,c,d,message\n", 1574 | "one,1,2,3.0,4,\n", 1575 | "two,5,6,,8,world\n", 1576 | "three,9,10,11.0,12,foo \n" 1577 | ] 1578 | } 1579 | ], 1580 | "source": [ 1581 | "# 設定 cols 參數，只寫出一部分的欄位\n", 1582 | "df\n", 1583 | "df.to_csv(\"ex5-1.csv\", index = False, cols = ['a', 'b', 'c']) # 好像無效呢?\n", 1584 | "!type \"ex5-1.csv\"" 1585 | ] 1586 | }, 1587 | { 1588 | "cell_type": "code", 1589 | "execution_count": 30, 1590 | "metadata": { 1591 | "collapsed": false 1592 | }, 1593 | "outputs": [ 1594 | { 1595 | "data": { 1596 | "text/plain": [ 1597 | "DatetimeIndex(['2000-01-01', '2000-01-02', '2000-01-03', '2000-01-04',\n", 1598 | " '2000-01-05', '2000-01-06', '2000-01-07'],\n", 1599 | " dtype='datetime64[ns]', freq='D')" 1600 | ] 1601 | }, 1602 | "execution_count": 30, 1603 | "metadata": {}, 1604 | "output_type": "execute_result" 1605 | } 1606 | ], 1607 | "source": [ 1608 | "# Series 也有to_csv方法\n", 1609 | "dates = pd.date_range('1/1/2000', periods = 7)\n", 1610 | "dates" 1611 | ] 1612 | }, 1613 | { 1614 | "cell_type": "code", 1615 | "execution_count": 31, 1616 | "metadata": { 1617 | "collapsed": false 1618 | }, 1619 | "outputs": [ 1620 | { 1621 | "data": { 1622 | "text/plain": [ 1623 | "2000-01-01 0\n", 1624 | "2000-01-02 1\n", 1625 | "2000-01-03 2\n", 1626 | "2000-01-04 3\n", 1627 | "2000-01-05 4\n", 1628 | "2000-01-06 5\n", 1629 | "2000-01-07 6\n", 1630 | "Freq: D, dtype: int32" 1631 | ] 1632 | }, 1633 | "execution_count": 31, 1634 | "metadata": {}, 1635 | "output_type": "execute_result" 1636 | } 1637 | ], 1638 | "source": [ 1639 | "ts = Series(np.arange(7), index = dates)\n", 1640 | "ts" 1641 | ] 1642 | }, 1643 | { 1644 | "cell_type": "code", 1645 | "execution_count": 32, 1646 | "metadata": { 1647 | "collapsed": false 1648 | }, 1649 | "outputs": [ 1650 | { 1651 | "name": "stdout", 1652 | "output_type": "stream", 1653 | "text": [ 1654 | "2000-01-01,0\n", 1655 | "2000-01-02,1\n", 1656 | "2000-01-03,2\n", 1657 | "2000-01-04,3\n", 1658 | "2000-01-05,4\n", 1659 | "2000-01-06,5\n", 1660 | "2000-01-07,6\n" 1661 | ] 1662 | } 1663 | ], 1664 | "source": [ 1665 | "# Series物件 也有to_csv方法\n", 1666 | "ts.to_csv('treseries.csv')\n", 1667 | "!type \"treseries.csv\"" 1668 | ] 1669 | }, 1670 | { 1671 | "cell_type": "code", 1672 | "execution_count": 33, 1673 | "metadata": { 1674 | "collapsed": false 1675 | }, 1676 | "outputs": [ 1677 | { 1678 | "name": "stdout", 1679 | "output_type": "stream", 1680 | "text": [ 1681 | "2000-01-01,0\n", 1682 | "2000-01-02,1\n", 1683 | "2000-01-03,2\n", 1684 | "2000-01-04,3\n", 1685 | "2000-01-05,4\n", 1686 | "2000-01-06,5\n", 1687 | "2000-01-07,6\n" 1688 | ] 1689 | } 1690 | ], 1691 | "source": [ 1692 | "# Series類別 也有to_csv方法 (頂層)\n", 1693 | "Series.to_csv(ts, 'treseries.csv')\n", 1694 | "!type \"treseries.csv" 1695 | ] 1696 | }, 1697 | { 1698 | "cell_type": "code", 1699 | "execution_count": 34, 1700 | "metadata": { 1701 | "collapsed": false 1702 | }, 1703 | "outputs": [ 1704 | { 1705 | "data": { 1706 | "text/plain": [ 1707 | "2000-01-01 0\n", 1708 | "2000-01-02 1\n", 1709 | "2000-01-03 2\n", 1710 | "2000-01-04 3\n", 1711 | "2000-01-05 4\n", 1712 | "2000-01-06 5\n", 1713 | "2000-01-07 6\n", 1714 | "dtype: int64" 1715 | ] 1716 | }, 1717 | "execution_count": 34, 1718 | "metadata": {}, 1719 | "output_type": "execute_result" 1720 | } 1721 | ], 1722 | "source": [ 1723 | "# 使用 from_csv 將檔案讀入成為 Series\n", 1724 | "# 有 date欄位，須設定 parse_dates 參數\n", 1725 | "ts = Series.from_csv('treseries.csv', parse_dates = True)\n", 1726 | "ts" 1727 | ] 1728 | }, 1729 | { 1730 | "cell_type": "markdown", 1731 | "metadata": {}, 1732 | "source": [ 1733 | "## JSON(JavaScript Object Notation)數據" 1734 | ] 1735 | }, 1736 | { 1737 | "cell_type": "code", 1738 | "execution_count": 35, 1739 | "metadata": { 1740 | "collapsed": true 1741 | }, 1742 | "outputs": [], 1743 | "source": [ 1744 | "obj = \"\"\"\n", 1745 | "{\n", 1746 | "\"name\": \"Wes\", \n", 1747 | "\"place_lived\": [\"United States\", \"Spain\", \"Germany\"],\n", 1748 | "\"pet\": null,\n", 1749 | "\"siblings\": [{\"name\": \"Scott\", \"age\": 25, \"pet\": \"Zuko\"}, {\"name\": \"Wei\", \"age\": 25, \"pet\": \"Cisco\"}]\n", 1750 | "}\n", 1751 | "\"\"\"" 1752 | ] 1753 | }, 1754 | { 1755 | "cell_type": "code", 1756 | "execution_count": 36, 1757 | "metadata": { 1758 | "collapsed": false 1759 | }, 1760 | "outputs": [ 1761 | { 1762 | "data": { 1763 | "text/plain": [ 1764 | "{'name': 'Wes',\n", 1765 | " 'pet': None,\n", 1766 | " 'place_lived': ['United States', 'Spain', 'Germany'],\n", 1767 | " 'siblings': [{'age': 25, 'name': 'Scott', 'pet': 'Zuko'},\n", 1768 | " {'age': 25, 'name': 'Wei', 'pet': 'Cisco'}]}" 1769 | ] 1770 | }, 1771 | "execution_count": 36, 1772 | "metadata": {}, 1773 | "output_type": "execute_result" 1774 | } 1775 | ], 1776 | "source": [ 1777 | "# 用 json.loads 可將JSON字串還原成 dict物件\n", 1778 | "import json\n", 1779 | "\n", 1780 | "result = json.loads(obj)\n", 1781 | "result" 1782 | ] 1783 | }, 1784 | { 1785 | "cell_type": "code", 1786 | "execution_count": 37, 1787 | "metadata": { 1788 | "collapsed": false 1789 | }, 1790 | "outputs": [ 1791 | { 1792 | "data": { 1793 | "text/plain": [ 1794 | "dict" 1795 | ] 1796 | }, 1797 | "execution_count": 37, 1798 | "metadata": {}, 1799 | "output_type": "execute_result" 1800 | } 1801 | ], 1802 | "source": [ 1803 | "# JSON物件其實是 dict 物件\n", 1804 | "type(result)" 1805 | ] 1806 | }, 1807 | { 1808 | "cell_type": "code", 1809 | "execution_count": 38, 1810 | "metadata": { 1811 | "collapsed": false 1812 | }, 1813 | "outputs": [ 1814 | { 1815 | "data": { 1816 | "text/plain": [ 1817 | "int" 1818 | ] 1819 | }, 1820 | "execution_count": 38, 1821 | "metadata": {}, 1822 | "output_type": "execute_result" 1823 | } 1824 | ], 1825 | "source": [ 1826 | "# 使用索引，可以探及 dict內部的資料\n", 1827 | "type(result['siblings'][0]['age'])" 1828 | ] 1829 | }, 1830 | { 1831 | "cell_type": "code", 1832 | "execution_count": 39, 1833 | "metadata": { 1834 | "collapsed": false 1835 | }, 1836 | "outputs": [ 1837 | { 1838 | "data": { 1839 | "text/plain": [ 1840 | "'{\"place_lived\": [\"United States\", \"Spain\", \"Germany\"], \"siblings\": [{\"pet\": \"Zuko\", \"age\": 25, \"name\": \"Scott\"}, {\"pet\": \"Cisco\", \"age\": 25, \"name\": \"Wei\"}], \"pet\": null, \"name\": \"Wes\"}'" 1841 | ] 1842 | }, 1843 | "execution_count": 39, 1844 | "metadata": {}, 1845 | "output_type": "execute_result" 1846 | } 1847 | ], 1848 | "source": [ 1849 | "# json.dumps 可將dict物件轉換成 JSON字串\n", 1850 | "# json字串 和json物件 需區分清楚\n", 1851 | "# json物件 其實就是 dict\n", 1852 | "json.dumps(result)" 1853 | ] 1854 | }, 1855 | { 1856 | "cell_type": "code", 1857 | "execution_count": 40, 1858 | "metadata": { 1859 | "collapsed": false 1860 | }, 1861 | "outputs": [ 1862 | { 1863 | "data": { 1864 | "text/plain": [ 1865 | "[{'age': 25, 'name': 'Scott', 'pet': 'Zuko'},\n", 1866 | " {'age': 25, 'name': 'Wei', 'pet': 'Cisco'}]" 1867 | ] 1868 | }, 1869 | "execution_count": 40, 1870 | "metadata": {}, 1871 | "output_type": "execute_result" 1872 | } 1873 | ], 1874 | "source": [ 1875 | "result['siblings']" 1876 | ] 1877 | }, 1878 | { 1879 | "cell_type": "code", 1880 | "execution_count": 41, 1881 | "metadata": { 1882 | "collapsed": false 1883 | }, 1884 | "outputs": [ 1885 | { 1886 | "data": { 1887 | "text/html": [ 1888 | "
\n", 1889 | "\n", 1890 | " \n", 1891 | " \n", 1892 | " \n", 1893 | " \n", 1894 | " \n", 1895 | " \n", 1896 | " \n", 1897 | " \n", 1898 | " \n", 1899 | " \n", 1900 | " \n", 1901 | " \n", 1902 | " \n", 1903 | " \n", 1904 | " \n", 1905 | " \n", 1906 | " \n", 1907 | " \n", 1908 | " \n", 1909 | " \n", 1910 | " \n", 1911 | " \n", 1912 | " \n", 1913 | " \n", 1914 | "
01
age2525
nameScottWei
petZukoCisco
\n", 1915 | "
" 1916 | ], 1917 | "text/plain": [ 1918 | " 0 1\n", 1919 | "age 25 25\n", 1920 | "name Scott Wei\n", 1921 | "pet Zuko Cisco" 1922 | ] 1923 | }, 1924 | "execution_count": 41, 1925 | "metadata": {}, 1926 | "output_type": "execute_result" 1927 | } 1928 | ], 1929 | "source": [ 1930 | "# 以JSON物件建構DataFrame\n", 1931 | "df_siblings = DataFrame(result['siblings'], columns = ['age', 'name', 'pet']).T\n", 1932 | "df_siblings" 1933 | ] 1934 | }, 1935 | { 1936 | "cell_type": "code", 1937 | "execution_count": 42, 1938 | "metadata": { 1939 | "collapsed": false 1940 | }, 1941 | "outputs": [ 1942 | { 1943 | "data": { 1944 | "text/plain": [ 1945 | "'{\"0\":{\"age\":25,\"name\":\"Scott\",\"pet\":\"Zuko\"},\"1\":{\"age\":25,\"name\":\"Wei\",\"pet\":\"Cisco\"}}'" 1946 | ] 1947 | }, 1948 | "execution_count": 42, 1949 | "metadata": {}, 1950 | "output_type": "execute_result" 1951 | } 1952 | ], 1953 | "source": [ 1954 | "# DataFrame有 to_json() 方法，可將DataFrame序列化\n", 1955 | "siblings_json_string = df_siblings.to_json()\n", 1956 | "siblings_json_string" 1957 | ] 1958 | }, 1959 | { 1960 | "cell_type": "code", 1961 | "execution_count": 43, 1962 | "metadata": { 1963 | "collapsed": false 1964 | }, 1965 | "outputs": [ 1966 | { 1967 | "data": { 1968 | "text/plain": [ 1969 | "{'0': {'age': 25, 'name': 'Scott', 'pet': 'Zuko'},\n", 1970 | " '1': {'age': 25, 'name': 'Wei', 'pet': 'Cisco'}}" 1971 | ] 1972 | }, 1973 | "execution_count": 43, 1974 | "metadata": {}, 1975 | "output_type": "execute_result" 1976 | } 1977 | ], 1978 | "source": [ 1979 | "siblings_json = json.loads(siblings_json_string)\n", 1980 | "siblings_json" 1981 | ] 1982 | }, 1983 | { 1984 | "cell_type": "code", 1985 | "execution_count": 44, 1986 | "metadata": { 1987 | "collapsed": false 1988 | }, 1989 | "outputs": [ 1990 | { 1991 | "data": { 1992 | "text/html": [ 1993 | "
\n", 1994 | "\n", 1995 | " \n", 1996 | " \n", 1997 | " \n", 1998 | " \n", 1999 | " \n", 2000 | " \n", 2001 | " \n", 2002 | " \n", 2003 | " \n", 2004 | " \n", 2005 | " \n", 2006 | " \n", 2007 | " \n", 2008 | " \n", 2009 | " \n", 2010 | " \n", 2011 | " \n", 2012 | " \n", 2013 | " \n", 2014 | " \n", 2015 | " \n", 2016 | " \n", 2017 | " \n", 2018 | " \n", 2019 | "
01
age2525
nameScottWei
petZukoCisco
\n", 2020 | "
" 2021 | ], 2022 | "text/plain": [ 2023 | " 0 1\n", 2024 | "age 25 25\n", 2025 | "name Scott Wei\n", 2026 | "pet Zuko Cisco" 2027 | ] 2028 | }, 2029 | "execution_count": 44, 2030 | "metadata": {}, 2031 | "output_type": "execute_result" 2032 | } 2033 | ], 2034 | "source": [ 2035 | "# DataFrame有 from_dict() 方法，可反序列化\n", 2036 | "df_siblings = DataFrame.from_dict(siblings_json)\n", 2037 | "df_siblings" 2038 | ] 2039 | }, 2040 | { 2041 | "cell_type": "markdown", 2042 | "metadata": {}, 2043 | "source": [ 2044 | "## Web訊息收集" 2045 | ] 2046 | }, 2047 | { 2048 | "cell_type": "markdown", 2049 | "metadata": {}, 2050 | "source": [ 2051 | "[Yahoo股票資料抓取](../%E7%B7%B4%E7%BF%92%20-%20%E8%82%A1%E7%A5%A8%E8%B3%87%E6%96%99%E5%BD%99%E6%95%B4_Yahoo%E8%82%A1%E5%B8%82%20-%20%E5%95%8F%E9%A1%8C.ipynb)" 2052 | ] 2053 | }, 2054 | { 2055 | "cell_type": "markdown", 2056 | "metadata": {}, 2057 | "source": [ 2058 | "## 二進制數據格式" 2059 | ] 2060 | }, 2061 | { 2062 | "cell_type": "code", 2063 | "execution_count": 45, 2064 | "metadata": { 2065 | "collapsed": false 2066 | }, 2067 | "outputs": [ 2068 | { 2069 | "data": { 2070 | "text/html": [ 2071 | "
\n", 2072 | "\n", 2073 | " \n", 2074 | " \n", 2075 | " \n", 2076 | " \n", 2077 | " \n", 2078 | " \n", 2079 | " \n", 2080 | " \n", 2081 | " \n", 2082 | " \n", 2083 | " \n", 2084 | " \n", 2085 | " \n", 2086 | " \n", 2087 | " \n", 2088 | " \n", 2089 | " \n", 2090 | " \n", 2091 | " \n", 2092 | " \n", 2093 | " \n", 2094 | " \n", 2095 | " \n", 2096 | " \n", 2097 | " \n", 2098 | " \n", 2099 | " \n", 2100 | " \n", 2101 | " \n", 2102 | " \n", 2103 | " \n", 2104 | " \n", 2105 | " \n", 2106 | " \n", 2107 | " \n", 2108 | " \n", 2109 | "
abcdmessage
01234hello
15678world
29101112foo
\n", 2110 | "
" 2111 | ], 2112 | "text/plain": [ 2113 | " a b c d message\n", 2114 | "0 1 2 3 4 hello\n", 2115 | "1 5 6 7 8 world\n", 2116 | "2 9 10 11 12 foo" 2117 | ] 2118 | }, 2119 | "execution_count": 45, 2120 | "metadata": {}, 2121 | "output_type": "execute_result" 2122 | } 2123 | ], 2124 | "source": [ 2125 | "# pandas物件都有一個 save方法，可以將物件數據以pickle的形式保存到硬碟\n", 2126 | "df = pd.read_csv('ex1.csv')\n", 2127 | "df" 2128 | ] 2129 | }, 2130 | { 2131 | "cell_type": "code", 2132 | "execution_count": 46, 2133 | "metadata": { 2134 | "collapsed": false 2135 | }, 2136 | "outputs": [ 2137 | { 2138 | "data": { 2139 | "text/plain": [ 2140 | "pandas.core.frame.DataFrame" 2141 | ] 2142 | }, 2143 | "execution_count": 46, 2144 | "metadata": {}, 2145 | "output_type": "execute_result" 2146 | } 2147 | ], 2148 | "source": [ 2149 | "type(df)" 2150 | ] 2151 | }, 2152 | { 2153 | "cell_type": "code", 2154 | "execution_count": 47, 2155 | "metadata": { 2156 | "collapsed": false 2157 | }, 2158 | "outputs": [], 2159 | "source": [ 2160 | "# 輸出 pickle資料到檔案\n", 2161 | "import pickle\n", 2162 | "df.to_pickle('ex1.pickle')\n", 2163 | "df = None\n", 2164 | "del df" 2165 | ] 2166 | }, 2167 | { 2168 | "cell_type": "code", 2169 | "execution_count": 48, 2170 | "metadata": { 2171 | "collapsed": false 2172 | }, 2173 | "outputs": [ 2174 | { 2175 | "data": { 2176 | "text/html": [ 2177 | "
\n", 2178 | "\n", 2179 | " \n", 2180 | " \n", 2181 | " \n", 2182 | " \n", 2183 | " \n", 2184 | " \n", 2185 | " \n", 2186 | " \n", 2187 | " \n", 2188 | " \n", 2189 | " \n", 2190 | " \n", 2191 | " \n", 2192 | " \n", 2193 | " \n", 2194 | " \n", 2195 | " \n", 2196 | " \n", 2197 | " \n", 2198 | " \n", 2199 | " \n", 2200 | " \n", 2201 | " \n", 2202 | " \n", 2203 | " \n", 2204 | " \n", 2205 | " \n", 2206 | " \n", 2207 | " \n", 2208 | " \n", 2209 | " \n", 2210 | " \n", 2211 | " \n", 2212 | " \n", 2213 | " \n", 2214 | " \n", 2215 | "
abcdmessage
01234hello
15678world
29101112foo
\n", 2216 | "
" 2217 | ], 2218 | "text/plain": [ 2219 | " a b c d message\n", 2220 | "0 1 2 3 4 hello\n", 2221 | "1 5 6 7 8 world\n", 2222 | "2 9 10 11 12 foo" 2223 | ] 2224 | }, 2225 | "execution_count": 48, 2226 | "metadata": {}, 2227 | "output_type": "execute_result" 2228 | } 2229 | ], 2230 | "source": [ 2231 | "# 讀入 pickle檔案資料成為物件 \n", 2232 | "df = pickle.load(open('ex1.pickle', 'rb'))\n", 2233 | "df" 2234 | ] 2235 | }, 2236 | { 2237 | "cell_type": "code", 2238 | "execution_count": 49, 2239 | "metadata": { 2240 | "collapsed": false 2241 | }, 2242 | "outputs": [ 2243 | { 2244 | "data": { 2245 | "text/plain": [ 2246 | "pandas.core.frame.DataFrame" 2247 | ] 2248 | }, 2249 | "execution_count": 49, 2250 | "metadata": {}, 2251 | "output_type": "execute_result" 2252 | } 2253 | ], 2254 | "source": [ 2255 | "type(df)" 2256 | ] 2257 | }, 2258 | { 2259 | "cell_type": "markdown", 2260 | "metadata": {}, 2261 | "source": [ 2262 | "### 讀取 Microsoft Excel文件" 2263 | ] 2264 | }, 2265 | { 2266 | "cell_type": "code", 2267 | "execution_count": 50, 2268 | "metadata": { 2269 | "collapsed": false 2270 | }, 2271 | "outputs": [ 2272 | { 2273 | "data": { 2274 | "text/html": [ 2275 | "
\n", 2276 | "\n", 2277 | " \n", 2278 | " \n", 2279 | " \n", 2280 | " \n", 2281 | " \n", 2282 | " \n", 2283 | " \n", 2284 | " \n", 2285 | " \n", 2286 | " \n", 2287 | " \n", 2288 | " \n", 2289 | " \n", 2290 | " \n", 2291 | " \n", 2292 | " \n", 2293 | " \n", 2294 | " \n", 2295 | " \n", 2296 | " \n", 2297 | " \n", 2298 | " \n", 2299 | " \n", 2300 | " \n", 2301 | " \n", 2302 | " \n", 2303 | " \n", 2304 | " \n", 2305 | "
時間溫度濕度
02016-02-01 10:35:00.0001240
12016-02-01 10:36:00.0001341
22016-02-01 10:36:59.9951442
\n", 2306 | "
" 2307 | ], 2308 | "text/plain": [ 2309 | " 時間 溫度 濕度\n", 2310 | "0 2016-02-01 10:35:00.000 12 40\n", 2311 | "1 2016-02-01 10:36:00.000 13 41\n", 2312 | "2 2016-02-01 10:36:59.995 14 42" 2313 | ] 2314 | }, 2315 | "execution_count": 50, 2316 | "metadata": {}, 2317 | "output_type": "execute_result" 2318 | } 2319 | ], 2320 | "source": [ 2321 | "# 使用 ExcelFile 方法\n", 2322 | "xls_file = pd.ExcelFile('test.xls', header = None)\n", 2323 | "table = xls_file.parse('Sheet1')\n", 2324 | "table" 2325 | ] 2326 | }, 2327 | { 2328 | "cell_type": "code", 2329 | "execution_count": 51, 2330 | "metadata": { 2331 | "collapsed": false 2332 | }, 2333 | "outputs": [ 2334 | { 2335 | "data": { 2336 | "text/plain": [ 2337 | "pandas.core.frame.DataFrame" 2338 | ] 2339 | }, 2340 | "execution_count": 51, 2341 | "metadata": {}, 2342 | "output_type": "execute_result" 2343 | } 2344 | ], 2345 | "source": [ 2346 | "type(table)" 2347 | ] 2348 | }, 2349 | { 2350 | "cell_type": "markdown", 2351 | "metadata": {}, 2352 | "source": [ 2353 | "## 使用數據庫" 2354 | ] 2355 | }, 2356 | { 2357 | "cell_type": "code", 2358 | "execution_count": 52, 2359 | "metadata": { 2360 | "collapsed": false 2361 | }, 2362 | "outputs": [ 2363 | { 2364 | "data": { 2365 | "text/plain": [ 2366 | "[('Atlanta', 'Georgia', 1.25, 6),\n", 2367 | " ('Tallahassee', 'Florida', 2.6, 3),\n", 2368 | " ('Sacramento', 'California', 1.7, 5)]" 2369 | ] 2370 | }, 2371 | "execution_count": 52, 2372 | "metadata": {}, 2373 | "output_type": "execute_result" 2374 | } 2375 | ], 2376 | "source": [ 2377 | "# 使用 SQLite3\n", 2378 | "\n", 2379 | "import sqlite3\n", 2380 | "\n", 2381 | "# 連接資料庫\n", 2382 | "con = sqlite3.connect(':memory:')\n", 2383 | "\n", 2384 | "# 建立資料表\n", 2385 | "query = \"\"\"\n", 2386 | "CREATE TABLE test\n", 2387 | "(a VARCHAR(20), b VARCHAR(20), c REAL, d INTEGER);\n", 2388 | "\"\"\"\n", 2389 | "con.execute(query)\n", 2390 | "con.commit()\n", 2391 | "\n", 2392 | "# 插入資料\n", 2393 | "data = [('Atlanta', 'Georgia', 1.25, 6), \n", 2394 | " ('Tallahassee', 'Florida', 2.6, 3), \n", 2395 | " ('Sacramento', 'California', 1.7, 5)]\n", 2396 | "stmt = \"INSERT INTO test VALUES(?, ?, ?, ?)\"\n", 2397 | "con.executemany(stmt, data)\n", 2398 | "con.commit()\n", 2399 | "\n", 2400 | "\n", 2401 | "# 查詢資料\n", 2402 | "cursor = con.execute('select * from test')\n", 2403 | "rows = cursor.fetchall()\n", 2404 | "rows" 2405 | ] 2406 | }, 2407 | { 2408 | "cell_type": "code", 2409 | "execution_count": 53, 2410 | "metadata": { 2411 | "collapsed": false 2412 | }, 2413 | "outputs": [ 2414 | { 2415 | "data": { 2416 | "text/plain": [ 2417 | "(('a', None, None, None, None, None, None),\n", 2418 | " ('b', None, None, None, None, None, None),\n", 2419 | " ('c', None, None, None, None, None, None),\n", 2420 | " ('d', None, None, None, None, None, None))" 2421 | ] 2422 | }, 2423 | "execution_count": 53, 2424 | "metadata": {}, 2425 | "output_type": "execute_result" 2426 | } 2427 | ], 2428 | "source": [ 2429 | "# cursor.description 包含 欄位資訊\n", 2430 | "cursor.description" 2431 | ] 2432 | }, 2433 | { 2434 | "cell_type": "code", 2435 | "execution_count": 54, 2436 | "metadata": { 2437 | "collapsed": false 2438 | }, 2439 | "outputs": [ 2440 | { 2441 | "data": { 2442 | "text/html": [ 2443 | "
\n", 2444 | "\n", 2445 | " \n", 2446 | " \n", 2447 | " \n", 2448 | " \n", 2449 | " \n", 2450 | " \n", 2451 | " \n", 2452 | " \n", 2453 | " \n", 2454 | " \n", 2455 | " \n", 2456 | " \n", 2457 | " \n", 2458 | " \n", 2459 | " \n", 2460 | " \n", 2461 | " \n", 2462 | " \n", 2463 | " \n", 2464 | " \n", 2465 | " \n", 2466 | " \n", 2467 | " \n", 2468 | " \n", 2469 | " \n", 2470 | " \n", 2471 | " \n", 2472 | " \n", 2473 | " \n", 2474 | " \n", 2475 | " \n", 2476 | " \n", 2477 | "
abcd
0AtlantaGeorgia1.256
1TallahasseeFlorida2.603
2SacramentoCalifornia1.705
\n", 2478 | "
" 2479 | ], 2480 | "text/plain": [ 2481 | " a b c d\n", 2482 | "0 Atlanta Georgia 1.25 6\n", 2483 | "1 Tallahassee Florida 2.60 3\n", 2484 | "2 Sacramento California 1.70 5" 2485 | ] 2486 | }, 2487 | "execution_count": 54, 2488 | "metadata": {}, 2489 | "output_type": "execute_result" 2490 | } 2491 | ], 2492 | "source": [ 2493 | "# 用資料庫的資料建立 DataFrame\n", 2494 | "df = DataFrame(rows, columns = [f[0] for f in cursor.description])\n", 2495 | "df" 2496 | ] 2497 | }, 2498 | { 2499 | "cell_type": "code", 2500 | "execution_count": 55, 2501 | "metadata": { 2502 | "collapsed": false 2503 | }, 2504 | "outputs": [ 2505 | { 2506 | "data": { 2507 | "text/html": [ 2508 | "
\n", 2509 | "\n", 2510 | " \n", 2511 | " \n", 2512 | " \n", 2513 | " \n", 2514 | " \n", 2515 | " \n", 2516 | " \n", 2517 | " \n", 2518 | " \n", 2519 | " \n", 2520 | " \n", 2521 | " \n", 2522 | " \n", 2523 | " \n", 2524 | " \n", 2525 | " \n", 2526 | " \n", 2527 | " \n", 2528 | " \n", 2529 | " \n", 2530 | " \n", 2531 | " \n", 2532 | " \n", 2533 | " \n", 2534 | " \n", 2535 | " \n", 2536 | " \n", 2537 | " \n", 2538 | " \n", 2539 | " \n", 2540 | " \n", 2541 | " \n", 2542 | "
abcd
0AtlantaGeorgia1.256
1TallahasseeFlorida2.603
2SacramentoCalifornia1.705
\n", 2543 | "
" 2544 | ], 2545 | "text/plain": [ 2546 | " a b c d\n", 2547 | "0 Atlanta Georgia 1.25 6\n", 2548 | "1 Tallahassee Florida 2.60 3\n", 2549 | "2 Sacramento California 1.70 5" 2550 | ] 2551 | }, 2552 | "execution_count": 55, 2553 | "metadata": {}, 2554 | "output_type": "execute_result" 2555 | } 2556 | ], 2557 | "source": [ 2558 | "# 使用 pandas.io.sql 來讀取資料庫資料並創建 DataFrame\n", 2559 | "import pandas.io.sql as sql\n", 2560 | "df = sql.read_sql('select * from test', con)\n", 2561 | "df" 2562 | ] 2563 | } 2564 | ], 2565 | "metadata": { 2566 | "anaconda-cloud": {}, 2567 | "kernelspec": { 2568 | "display_name": "Python [default]", 2569 | "language": "python", 2570 | "name": "python3" 2571 | }, 2572 | "language_info": { 2573 | "codemirror_mode": { 2574 | "name": "ipython", 2575 | "version": 3 2576 | }, 2577 | "file_extension": ".py", 2578 | "mimetype": "text/x-python", 2579 | "name": "python", 2580 | "nbconvert_exporter": "python", 2581 | "pygments_lexer": "ipython3", 2582 | "version": "3.5.1" 2583 | } 2584 | }, 2585 | "nbformat": 4, 2586 | "nbformat_minor": 0 2587 | } 2588 | -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex1.csv: -------------------------------------------------------------------------------- 1 | a,b,c,d,message 2 | 1,2,3,4,hello 3 | 5,6,7,8,world 4 | 9,10,11,12,foo -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex1.pickle: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Wei1234c/Introduction_to_Pandas/af523d3dc81a1bf49a03740781b1a918ace2cfc9/notebooks/2. Pandas - IO tools/ex1.pickle -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex2.csv: -------------------------------------------------------------------------------- 1 | 1,2,3,4,hello 2 | 5,6,7,8,world 3 | 9,10,11,12,foo -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex3 - 1.csv: -------------------------------------------------------------------------------- 1 | A B C 2 | aaa -0.264 -1.026 -0.619 3 | bbb 0.927 0.302 -0.032 4 | ccc -0.265 -0.385 -0.217 5 | -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex3.csv: -------------------------------------------------------------------------------- 1 | key1,key2,value1,value2 2 | one,a,1,2 3 | one,b,3,4 4 | one,c,5,6 5 | one,d,7,8 6 | two,a,9,10 7 | two,b,11,12 8 | two,c,13,14 9 | two,d,15,16 -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex4.csv: -------------------------------------------------------------------------------- 1 | # hey! 2 | a,b,c,d,message 3 | # just wanted to make things more difficult for you 4 | # who read CSV files with computers, anyway? 5 | 1,2,3,4,hello 6 | 5,6,7,8,world 7 | 9,10,11,12,foo -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex5-1.csv: -------------------------------------------------------------------------------- 1 | something,a,b,c,d,message 2 | one,1,2,3.0,4, 3 | two,5,6,,8,world 4 | three,9,10,11.0,12,foo 5 | -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex5.csv: -------------------------------------------------------------------------------- 1 | something,a,b,c,d,message 2 | one,1,2,3,4,NA 3 | two,5,6,,8,world 4 | three,9,10,11,12,foo -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex6-o.csv: -------------------------------------------------------------------------------- 1 | obj = """ { "name": "Wes", "place_lived": ["United States", "Spain", "Germany"], "pet": null, "siblings": [{"name": "Scott", "age": 25, "pet": "Zuko"}, {"name": "Wei", "age": 25, "pet": "Cisco"}] } """ -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/ex6.csv: -------------------------------------------------------------------------------- 1 | something;a;b;c;d;message 2 | one;1;2;3;4;NA 3 | two;5;6;;8;world 4 | three;9;10;11;12;foo -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/mta.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 373889 5 | 6 | Metro-North Railroad 7 | 12 8 | 9 | -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/mydata.h5: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Wei1234c/Introduction_to_Pandas/af523d3dc81a1bf49a03740781b1a918ace2cfc9/notebooks/2. Pandas - IO tools/mydata.h5 -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/test.xls: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Wei1234c/Introduction_to_Pandas/af523d3dc81a1bf49a03740781b1a918ace2cfc9/notebooks/2. Pandas - IO tools/test.xls -------------------------------------------------------------------------------- /notebooks/2. Pandas - IO tools/treseries.csv: -------------------------------------------------------------------------------- 1 | 2000-01-01,0 2 | 2000-01-02,1 3 | 2000-01-03,2 4 | 2000-01-04,3 5 | 2000-01-05,4 6 | 2000-01-06,5 7 | 2000-01-07,6 8 | -------------------------------------------------------------------------------- /notebooks/練習 - 股票資料彙整_YahooFinance - 問題.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 練習 - 股票資料彙整_YahooFinance - 問題" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": { 13 | "collapsed": false 14 | }, 15 | "source": [ 16 | "### 問題: \n", 17 | "使用 pandas_datareader.data.DataReader 抓取 2356.TW, 1566.TWO 最近一個月的股價資料 " 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "參考資料: \n", 25 | "http://pandas.pydata.org/pandas-docs/stable/remote_data.html \n", 26 | "http://www.predream.org/show-58-171-1.html \n", 27 | "http://stackoverflow.com/questions/22991567/pandas-yahoo-finance-datareader" 28 | ] 29 | } 30 | ], 31 | "metadata": { 32 | "kernelspec": { 33 | "display_name": "Python 3", 34 | "language": "python", 35 | "name": "python3" 36 | }, 37 | "language_info": { 38 | "codemirror_mode": { 39 | "name": "ipython", 40 | "version": 3 41 | }, 42 | "file_extension": ".py", 43 | "mimetype": "text/x-python", 44 | "name": "python", 45 | "nbconvert_exporter": "python", 46 | "pygments_lexer": "ipython3", 47 | "version": "3.5.1" 48 | } 49 | }, 50 | "nbformat": 4, 51 | "nbformat_minor": 0 52 | } 53 | -------------------------------------------------------------------------------- /notebooks/練習 - 股票資料彙整_YahooFinance - 解答.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 練習 - 股票資料彙整_YahooFinance - 解答\n", 8 | "http://pandas.pydata.org/pandas-docs/stable/remote_data.html \n", 9 | "http://www.predream.org/show-58-171-1.html \n", 10 | "http://stackoverflow.com/questions/22991567/pandas-yahoo-finance-datareader" 11 | ] 12 | }, 13 | { 14 | "cell_type": "code", 15 | "execution_count": 1, 16 | "metadata": { 17 | "collapsed": false 18 | }, 19 | "outputs": [], 20 | "source": [ 21 | "import numpy as np\n", 22 | "import pandas as pd\n", 23 | "from pandas import Series, DataFrame\n", 24 | "import datetime\n", 25 | "import timeit" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 2, 31 | "metadata": { 32 | "collapsed": false 33 | }, 34 | "outputs": [], 35 | "source": [ 36 | "from pandas_datareader import data, wb\n", 37 | "import datetime \n", 38 | "\n", 39 | "def getWebData(name, \n", 40 | " start = datetime.date(1970, 1, 1), \n", 41 | " end = datetime.date.today(), \n", 42 | " data_source = 'yahoo', \n", 43 | " retry_count=3, \n", 44 | " pause=0.001):\n", 45 | " \n", 46 | " df = data.DataReader(name = name, \n", 47 | " data_source = data_source,\n", 48 | " start = start,\n", 49 | " end = end,\n", 50 | " retry_count = retry_count,\n", 51 | " pause = pause\n", 52 | " ) \n", 53 | "\n", 54 | " df = df.to_frame()\n", 55 | " df.index.names = ['Date', 'Name'] \n", 56 | " \n", 57 | " return df" 58 | ] 59 | }, 60 | { 61 | "cell_type": "markdown", 62 | "metadata": {}, 63 | "source": [ 64 | "## Main" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 3, 70 | "metadata": { 71 | "collapsed": false 72 | }, 73 | "outputs": [], 74 | "source": [ 75 | "def fetchAndStoreStockData(stocks):\n", 76 | " \n", 77 | "# start = datetime.datetime(1965, 1, 1)\n", 78 | "# end = datetime.datetime(2013, 1, 1) \n", 79 | " df = getWebData(stocks) \n", 80 | "\n", 81 | " # Write to files __________________________ \n", 82 | " df.to_excel('Yahoo Finance{0}.xlsx'.format(''))" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 4, 88 | "metadata": { 89 | "collapsed": false 90 | }, 91 | "outputs": [ 92 | { 93 | "name": "stdout", 94 | "output_type": "stream", 95 | "text": [ 96 | "Wall time: 5.46 s\n" 97 | ] 98 | } 99 | ], 100 | "source": [ 101 | "if __name__ == '__main__':\n", 102 | " %time fetchAndStoreStockData(stocks = ['2356.TW', '1566.TWO'])" 103 | ] 104 | } 105 | ], 106 | "metadata": { 107 | "kernelspec": { 108 | "display_name": "Python [default]", 109 | "language": "python", 110 | "name": "python3" 111 | }, 112 | "language_info": { 113 | "codemirror_mode": { 114 | "name": "ipython", 115 | "version": 3 116 | }, 117 | "file_extension": ".py", 118 | "mimetype": "text/x-python", 119 | "name": "python", 120 | "nbconvert_exporter": "python", 121 | "pygments_lexer": "ipython3", 122 | "version": "3.5.1" 123 | } 124 | }, 125 | "nbformat": 4, 126 | "nbformat_minor": 0 127 | } 128 | -------------------------------------------------------------------------------- /notebooks/練習 - 股票資料彙整_Yahoo股市 - 問題.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 練習 - 股票資料彙整_Yahoo股市 - 問題" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": false 15 | }, 16 | "outputs": [ 17 | { 18 | "data": { 19 | "text/plain": [ 20 | "'https://tw.stock.yahoo.com/s/list.php?c=tse&pid=1'" 21 | ] 22 | }, 23 | "execution_count": 1, 24 | "metadata": {}, 25 | "output_type": "execute_result" 26 | } 27 | ], 28 | "source": [ 29 | "page = 1\n", 30 | "url = 'https://tw.stock.yahoo.com/s/list.php?c=tse&pid=' + str(page)\n", 31 | "url" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "## 目標: \n", 39 | "- 使用 Pandas，抓取上述 url 網頁中的股價資料\n", 40 | "- 將股票代號與名稱區隔為不同的欄位\n", 41 | "- 將資料儲存為 Excel 檔案\n", 42 | "- 須注意個欄位的格式，數字欄位的儲存格式應該為數字\n", 43 | "- 重排欄位順序為:'市場別', '股票代號', '股票名稱', '日期', '時間', '成交', '買進', '賣出', '漲跌', '張數', '昨收', '開盤', '最高', '最低'\n", 44 | "- Extra:\n", 45 | " - 匯集 Yahoo 股市 page 1~ 5 的資料 (pd.concat)\n", 46 | " - 依據股票代號的前兩碼，做 GroupBy 操作\n", 47 | " - merge ../data/個股_類別.xls(先解壓縮 個股_類別.rar) 中的資料之後，做 GroupBy 操作 " 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": null, 53 | "metadata": { 54 | "collapsed": false 55 | }, 56 | "outputs": [], 57 | "source": [] 58 | } 59 | ], 60 | "metadata": { 61 | "anaconda-cloud": {}, 62 | "kernelspec": { 63 | "display_name": "Python [default]", 64 | "language": "python", 65 | "name": "python3" 66 | }, 67 | "language_info": { 68 | "codemirror_mode": { 69 | "name": "ipython", 70 | "version": 3 71 | }, 72 | "file_extension": ".py", 73 | "mimetype": "text/x-python", 74 | "name": "python", 75 | "nbconvert_exporter": "python", 76 | "pygments_lexer": "ipython3", 77 | "version": "3.5.1" 78 | } 79 | }, 80 | "nbformat": 4, 81 | "nbformat_minor": 0 82 | } 83 | -------------------------------------------------------------------------------- /notebooks/練習 - 股票資料彙整_Yahoo股市 - 解答.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# 練習 - 股票資料彙整_Yahoo股市 - 解答" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import numpy as np\n", 19 | "import pandas as pd\n", 20 | "from pandas import Series, DataFrame" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "metadata": { 27 | "collapsed": true 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "import datetime" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "目標資料來源: \n", 39 | "https://tw.stock.yahoo.com/s/list.php?c=tse&pid=1" 40 | ] 41 | }, 42 | { 43 | "cell_type": "markdown", 44 | "metadata": {}, 45 | "source": [ 46 | "## 抓取網頁資料" 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": 3, 52 | "metadata": { 53 | "collapsed": true 54 | }, 55 | "outputs": [], 56 | "source": [ 57 | "import requests\n", 58 | "\n", 59 | "def get_yahoo_page_html(url): \n", 60 | " html = requests.get(url, headers={'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/53.0.2785.143 Safari/537.36'})\n", 61 | " return html.text" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 4, 67 | "metadata": { 68 | "collapsed": true 69 | }, 70 | "outputs": [], 71 | "source": [ 72 | "def getDataOnePage(html):\n", 73 | " targetTableIndex = 0\n", 74 | " table = pd.read_html(html,\n", 75 | " attrs = {'border': '1' , \n", 76 | " 'cellspacing': '0', \n", 77 | " 'cellpadding': '2', \n", 78 | " 'bgcolor': '#ffffff'},\n", 79 | " header = 0\n", 80 | " )[targetTableIndex]\n", 81 | " \n", 82 | " return table" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 5, 88 | "metadata": { 89 | "collapsed": false 90 | }, 91 | "outputs": [], 92 | "source": [ 93 | "def getDataOnePageTSE(page):\n", 94 | " url = 'https://tw.stock.yahoo.com/s/list.php?c=tse&pid=' + str(page) \n", 95 | " return getDataOnePage(html = get_yahoo_page_html(url))" 96 | ] 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 6, 101 | "metadata": { 102 | "collapsed": false 103 | }, 104 | "outputs": [], 105 | "source": [ 106 | "# 抓第一頁的資料\n", 107 | "df = getDataOnePageTSE(1)" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 7, 113 | "metadata": { 114 | "collapsed": false 115 | }, 116 | "outputs": [ 117 | { 118 | "data": { 119 | "text/html": [ 120 | "
\n", 121 | "\n", 122 | " \n", 123 | " \n", 124 | " \n", 125 | " \n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | "
選擇股票代號時間成交買進賣出漲跌張數昨收開盤最高最低凱基證券下單
195NaN4763 材料-KY14:30121.50121.5122.00.00994121.50121.0128.00120.50買 賣 張 零股交易
196NaN1598 岱宇13:3046.0546.0046.05▽0.5025246.5546.5046.9546.00買 賣 張 零股交易
197NaN1701 中化13:3018.1018.1018.15▽0.0521818.1518.2018.2018.05買 賣 張 零股交易
198NaN1707 葡萄王13:30261.00261.0261.5△3.5537257.50261.0262.50258.00買 賣 張 零股交易
199NaN1720 生達13:3033.4533.4033.450.0017933.4533.6033.6033.30買 賣 張 零股交易
\n", 223 | "
" 224 | ], 225 | "text/plain": [ 226 | " 選擇 股票代號 時間 成交 買進 賣出 漲跌 張數 昨收 開盤 \\\n", 227 | "195 NaN 4763 材料-KY 14:30 121.50 121.5 122.0 0.00 994 121.50 121.0 \n", 228 | "196 NaN 1598 岱宇 13:30 46.05 46.00 46.05 ▽0.50 252 46.55 46.50 \n", 229 | "197 NaN 1701 中化 13:30 18.10 18.10 18.15 ▽0.05 218 18.15 18.20 \n", 230 | "198 NaN 1707 葡萄王 13:30 261.00 261.0 261.5 △3.5 537 257.50 261.0 \n", 231 | "199 NaN 1720 生達 13:30 33.45 33.40 33.45 0.00 179 33.45 33.60 \n", 232 | "\n", 233 | " 最高 最低 凱基證券下單 \n", 234 | "195 128.00 120.50 買 賣 張 零股交易 \n", 235 | "196 46.95 46.00 買 賣 張 零股交易 \n", 236 | "197 18.20 18.05 買 賣 張 零股交易 \n", 237 | "198 262.50 258.00 買 賣 張 零股交易 \n", 238 | "199 33.60 33.30 買 賣 張 零股交易 " 239 | ] 240 | }, 241 | "execution_count": 7, 242 | "metadata": {}, 243 | "output_type": "execute_result" 244 | } 245 | ], 246 | "source": [ 247 | "df.tail()" 248 | ] 249 | }, 250 | { 251 | "cell_type": "code", 252 | "execution_count": 8, 253 | "metadata": { 254 | "collapsed": false 255 | }, 256 | "outputs": [], 257 | "source": [ 258 | "df.to_excel('stock.xlsx')" 259 | ] 260 | }, 261 | { 262 | "cell_type": "markdown", 263 | "metadata": {}, 264 | "source": [ 265 | "## 修整 DataFrame中的資料" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": 9, 271 | "metadata": { 272 | "collapsed": false 273 | }, 274 | "outputs": [], 275 | "source": [ 276 | "def fixTable(marketType, table, theDate = datetime.date.today()):\n", 277 | " \n", 278 | " fixedTable = table\n", 279 | " \n", 280 | " # Drop\n", 281 | " fixedTable.drop(['選擇', '凱基證券下單'], axis = 1, inplace = True)\n", 282 | " fixedTable.dropna(axis=0, how='all', inplace=True)\n", 283 | "\n", 284 | " # fill missing data\n", 285 | " fixedTable['股票代號名稱'] = fixedTable['股票代號']\n", 286 | " fixedTable['股票代號'] = fixedTable['股票代號名稱'].map(lambda x: x.split()[0])\n", 287 | " fixedTable['股票名稱'] = fixedTable['股票代號名稱'].map(lambda x: x.split()[1])\n", 288 | " fixedTable['日期'] = theDate\n", 289 | " fixedTable['市場別'] = marketType\n", 290 | " \n", 291 | " # data type\n", 292 | " fixedTable.replace('－', np.nan, inplace = True) \n", 293 | " \n", 294 | " fixedTable['股票代號'] = fixedTable['股票代號'].astype(str)\n", 295 | " fixedTable['時間'] = fixedTable['時間'].astype(datetime.time) \n", 296 | " fixedTable[['成交', '買進', '賣出', '張數', '昨收', '開盤', '最高', '最低']] = \\\n", 297 | " fixedTable[['成交', '買進', '賣出', '張數', '昨收', '開盤', '最高', '最低']].astype(float) \n", 298 | " \n", 299 | " fixedTable['漲跌'] = fixedTable['成交'] - fixedTable['昨收']\n", 300 | " fixedTable['漲跌'] = fixedTable['漲跌'].map(lambda x: round(x, 2))\n", 301 | " \n", 302 | " # sort\n", 303 | "# fixedTable.sort_values(by = '股票代號', inplace = True) \n", 304 | " \n", 305 | " # indexing\n", 306 | " fixedTable.index = Series(range(len(fixedTable)))\n", 307 | " fixedTable.index.name = '項次'\n", 308 | " fixedTable = fixedTable.reindex(columns = ['市場別', '股票代號', '股票名稱', '日期', '時間', '成交', '買進', '賣出', '漲跌', '張數', '昨收', '開盤', '最高', '最低'])\n", 309 | " \n", 310 | " return fixedTable" 311 | ] 312 | }, 313 | { 314 | "cell_type": "code", 315 | "execution_count": 10, 316 | "metadata": { 317 | "collapsed": false 318 | }, 319 | "outputs": [], 320 | "source": [ 321 | "df1 = fixTable('TSE', df)" 322 | ] 323 | }, 324 | { 325 | "cell_type": "code", 326 | "execution_count": 11, 327 | "metadata": { 328 | "collapsed": false 329 | }, 330 | "outputs": [ 331 | { 332 | "data": { 333 | "text/html": [ 334 | "
\n", 335 | "\n", 336 | " \n", 337 | " \n", 338 | " \n", 339 | " \n", 340 | " \n", 341 | " \n", 342 | " \n", 343 | " \n", 344 | " \n", 345 | " \n", 346 | " \n", 347 | " \n", 348 | " \n", 349 | " \n", 350 | " \n", 351 | " \n", 352 | " \n", 353 | " \n", 354 | " \n", 355 | " \n", 356 | " \n", 357 | " \n", 358 | " \n", 359 | " \n", 360 | " \n", 361 | " \n", 362 | " \n", 363 | " \n", 364 | " \n", 365 | " \n", 366 | " \n", 367 | " \n", 368 | " \n", 369 | " \n", 370 | " \n", 371 | " \n", 372 | " \n", 373 | " \n", 374 | " \n", 375 | " \n", 376 | " \n", 377 | " \n", 378 | " \n", 379 | " \n", 380 | " \n", 381 | " \n", 382 | " \n", 383 | " \n", 384 | " \n", 385 | " \n", 386 | " \n", 387 | " \n", 388 | " \n", 389 | " \n", 390 | " \n", 391 | " \n", 392 | " \n", 393 | " \n", 394 | " \n", 395 | " \n", 396 | " \n", 397 | " \n", 398 | " \n", 399 | " \n", 400 | " \n", 401 | " \n", 402 | " \n", 403 | " \n", 404 | " \n", 405 | " \n", 406 | " \n", 407 | " \n", 408 | " \n", 409 | " \n", 410 | " \n", 411 | " \n", 412 | " \n", 413 | " \n", 414 | " \n", 415 | " \n", 416 | " \n", 417 | " \n", 418 | " \n", 419 | " \n", 420 | " \n", 421 | " \n", 422 | " \n", 423 | " \n", 424 | " \n", 425 | " \n", 426 | " \n", 427 | " \n", 428 | " \n", 429 | " \n", 430 | " \n", 431 | " \n", 432 | " \n", 433 | " \n", 434 | " \n", 435 | " \n", 436 | " \n", 437 | " \n", 438 | " \n", 439 | " \n", 440 | " \n", 441 | " \n", 442 | " \n", 443 | " \n", 444 | " \n", 445 | " \n", 446 | " \n", 447 | " \n", 448 | " \n", 449 | " \n", 450 | " \n", 451 | " \n", 452 | " \n", 453 | " \n", 454 | " \n", 455 | " \n", 456 | " \n", 457 | " \n", 458 | " \n", 459 | "
市場別股票代號股票名稱日期時間成交買進賣出漲跌張數昨收開盤最高最低
項次
195TSE4763材料-KY2016-10-0414:30121.50121.5122.000.00994.0121.50121.0128.00120.50
196TSE1598岱宇2016-10-0413:3046.0546.046.05-0.50252.046.5546.546.9546.00
197TSE1701中化2016-10-0413:3018.1018.118.15-0.05218.018.1518.218.2018.05
198TSE1707葡萄王2016-10-0413:30261.00261.0261.503.50537.0257.50261.0262.50258.00
199TSE1720生達2016-10-0413:3033.4533.433.450.00179.033.4533.633.6033.30
\n", 460 | "
" 461 | ], 462 | "text/plain": [ 463 | " 市場別 股票代號 股票名稱 日期 時間 成交 買進 賣出 漲跌 張數 \\\n", 464 | "項次 \n", 465 | "195 TSE 4763 材料-KY 2016-10-04 14:30 121.50 121.5 122.00 0.00 994.0 \n", 466 | "196 TSE 1598 岱宇 2016-10-04 13:30 46.05 46.0 46.05 -0.50 252.0 \n", 467 | "197 TSE 1701 中化 2016-10-04 13:30 18.10 18.1 18.15 -0.05 218.0 \n", 468 | "198 TSE 1707 葡萄王 2016-10-04 13:30 261.00 261.0 261.50 3.50 537.0 \n", 469 | "199 TSE 1720 生達 2016-10-04 13:30 33.45 33.4 33.45 0.00 179.0 \n", 470 | "\n", 471 | " 昨收 開盤 最高 最低 \n", 472 | "項次 \n", 473 | "195 121.50 121.0 128.00 120.50 \n", 474 | "196 46.55 46.5 46.95 46.00 \n", 475 | "197 18.15 18.2 18.20 18.05 \n", 476 | "198 257.50 261.0 262.50 258.00 \n", 477 | "199 33.45 33.6 33.60 33.30 " 478 | ] 479 | }, 480 | "execution_count": 11, 481 | "metadata": {}, 482 | "output_type": "execute_result" 483 | } 484 | ], 485 | "source": [ 486 | "df1.tail(5)" 487 | ] 488 | }, 489 | { 490 | "cell_type": "markdown", 491 | "metadata": { 492 | "collapsed": true 493 | }, 494 | "source": [ 495 | "## 彙整 Yahoo 股市 page 1~ 5 的資料" 496 | ] 497 | }, 498 | { 499 | "cell_type": "code", 500 | "execution_count": 12, 501 | "metadata": { 502 | "collapsed": true 503 | }, 504 | "outputs": [], 505 | "source": [ 506 | "# 抓第一頁~第五頁的資料\n", 507 | "dfs = map(lambda p: fixTable('TSE', getDataOnePageTSE(p)) , range(1, 6))" 508 | ] 509 | }, 510 | { 511 | "cell_type": "code", 512 | "execution_count": 13, 513 | "metadata": { 514 | "collapsed": false 515 | }, 516 | "outputs": [ 517 | { 518 | "data": { 519 | "text/plain": [ 520 | "1000" 521 | ] 522 | }, 523 | "execution_count": 13, 524 | "metadata": {}, 525 | "output_type": "execute_result" 526 | } 527 | ], 528 | "source": [ 529 | "# Append 在一起\n", 530 | "df = pd.concat(dfs)\n", 531 | "len(df)" 532 | ] 533 | }, 534 | { 535 | "cell_type": "code", 536 | "execution_count": 14, 537 | "metadata": { 538 | "collapsed": false 539 | }, 540 | "outputs": [ 541 | { 542 | "data": { 543 | "text/html": [ 544 | "
\n", 545 | "\n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | " \n", 561 | " \n", 562 | " \n", 563 | " \n", 564 | " \n", 565 | " \n", 566 | " \n", 567 | " \n", 568 | " \n", 569 | " \n", 570 | " \n", 571 | " \n", 572 | " \n", 573 | " \n", 574 | " \n", 575 | " \n", 576 | " \n", 577 | " \n", 578 | " \n", 579 | " \n", 580 | " \n", 581 | " \n", 582 | " \n", 583 | " \n", 584 | " \n", 585 | " \n", 586 | " \n", 587 | " \n", 588 | " \n", 589 | " \n", 590 | " \n", 591 | " \n", 592 | " \n", 593 | " \n", 594 | " \n", 595 | " \n", 596 | " \n", 597 | " \n", 598 | " \n", 599 | " \n", 600 | " \n", 601 | " \n", 602 | " \n", 603 | " \n", 604 | " \n", 605 | " \n", 606 | " \n", 607 | " \n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | " \n", 618 | " \n", 619 | " \n", 620 | " \n", 621 | " \n", 622 | " \n", 623 | " \n", 624 | " \n", 625 | " \n", 626 | " \n", 627 | " \n", 628 | " \n", 629 | " \n", 630 | " \n", 631 | " \n", 632 | " \n", 633 | " \n", 634 | " \n", 635 | " \n", 636 | " \n", 637 | " \n", 638 | " \n", 639 | " \n", 640 | " \n", 641 | " \n", 642 | " \n", 643 | " \n", 644 | " \n", 645 | " \n", 646 | " \n", 647 | " \n", 648 | " \n", 649 | " \n", 650 | " \n", 651 | " \n", 652 | "
市場別股票代號股票名稱日期時間成交買進賣出漲跌張數昨收開盤最高最低
902TSE9941裕融2016-10-0413:3071.471.371.400.1171.071.371.371.471.2
903TSE9942茂順2016-10-0413:1886.985.986.401.637.085.385.886.985.2
904TSE9944新麗2016-10-0413:3024.224.224.50-0.3110.024.524.524.624.2
905TSE9945潤泰新2016-10-0414:3037.737.737.75-1.17229.038.838.638.637.6
906TSE9955佳龍2016-10-0413:3017.617.617.650.073.017.617.817.917.4
\n", 653 | "
" 654 | ], 655 | "text/plain": [ 656 | " 市場別 股票代號 股票名稱 日期 時間 成交 買進 賣出 漲跌 張數 昨收 \\\n", 657 | "902 TSE 9941 裕融 2016-10-04 13:30 71.4 71.3 71.40 0.1 171.0 71.3 \n", 658 | "903 TSE 9942 茂順 2016-10-04 13:18 86.9 85.9 86.40 1.6 37.0 85.3 \n", 659 | "904 TSE 9944 新麗 2016-10-04 13:30 24.2 24.2 24.50 -0.3 110.0 24.5 \n", 660 | "905 TSE 9945 潤泰新 2016-10-04 14:30 37.7 37.7 37.75 -1.1 7229.0 38.8 \n", 661 | "906 TSE 9955 佳龍 2016-10-04 13:30 17.6 17.6 17.65 0.0 73.0 17.6 \n", 662 | "\n", 663 | " 開盤 最高 最低 \n", 664 | "902 71.3 71.4 71.2 \n", 665 | "903 85.8 86.9 85.2 \n", 666 | "904 24.5 24.6 24.2 \n", 667 | "905 38.6 38.6 37.6 \n", 668 | "906 17.8 17.9 17.4 " 669 | ] 670 | }, 671 | "execution_count": 14, 672 | "metadata": {}, 673 | "output_type": "execute_result" 674 | } 675 | ], 676 | "source": [ 677 | "df.index = pd.Index(range(len(df))) # 重新編排 row index 編號\n", 678 | "df = df[df['股票代號'].str.len() <= 4] # 濾除 權證 資料\n", 679 | "df.tail()" 680 | ] 681 | }, 682 | { 683 | "cell_type": "markdown", 684 | "metadata": {}, 685 | "source": [ 686 | "## 抓取 類股 資料" 687 | ] 688 | }, 689 | { 690 | "cell_type": "code", 691 | "execution_count": 15, 692 | "metadata": { 693 | "collapsed": false 694 | }, 695 | "outputs": [ 696 | { 697 | "data": { 698 | "text/html": [ 699 | "
\n", 700 | "\n", 701 | " \n", 702 | " \n", 703 | " \n", 704 | " \n", 705 | " \n", 706 | " \n", 707 | " \n", 708 | " \n", 709 | " \n", 710 | " \n", 711 | " \n", 712 | " \n", 713 | " \n", 714 | " \n", 715 | " \n", 716 | " \n", 717 | " \n", 718 | " \n", 719 | " \n", 720 | " \n", 721 | " \n", 722 | " \n", 723 | " \n", 724 | " \n", 725 | " \n", 726 | " \n", 727 | " \n", 728 | " \n", 729 | " \n", 730 | " \n", 731 | " \n", 732 | " \n", 733 | " \n", 734 | " \n", 735 | " \n", 736 | " \n", 737 | " \n", 738 | " \n", 739 | " \n", 740 | " \n", 741 | " \n", 742 | " \n", 743 | " \n", 744 | " \n", 745 | " \n", 746 | " \n", 747 | " \n", 748 | " \n", 749 | " \n", 750 | " \n", 751 | " \n", 752 | " \n", 753 | "
市場別_ID類股別_ID個股_代號個股_名稱類股_名稱
2934527272861P國泰RG櫃認售
2934627272863P國泰RJ櫃認售
2934727272895P元大P3櫃認售
2934827272901P工銀QM櫃認售
2934927272953P日盛QW櫃認售
\n", 754 | "
" 755 | ], 756 | "text/plain": [ 757 | " 市場別_ID 類股別_ID 個股_代號 個股_名稱 類股_名稱\n", 758 | "29345 2 72 72861P 國泰RG 櫃認售\n", 759 | "29346 2 72 72863P 國泰RJ 櫃認售\n", 760 | "29347 2 72 72895P 元大P3 櫃認售\n", 761 | "29348 2 72 72901P 工銀QM 櫃認售\n", 762 | "29349 2 72 72953P 日盛QW 櫃認售" 763 | ] 764 | }, 765 | "execution_count": 15, 766 | "metadata": {}, 767 | "output_type": "execute_result" 768 | } 769 | ], 770 | "source": [ 771 | "df_類股 = pd.read_excel('..\\\\data\\個股_類別.xls') # 需先解壓縮 個股_類別.rar\n", 772 | "df_類股.tail()" 773 | ] 774 | }, 775 | { 776 | "cell_type": "markdown", 777 | "metadata": {}, 778 | "source": [ 779 | "## Merge" 780 | ] 781 | }, 782 | { 783 | "cell_type": "code", 784 | "execution_count": 16, 785 | "metadata": { 786 | "collapsed": false 787 | }, 788 | "outputs": [ 789 | { 790 | "data": { 791 | "text/html": [ 792 | "
\n", 793 | "\n", 794 | " \n", 795 | " \n", 796 | " \n", 797 | " \n", 798 | " \n", 799 | " \n", 800 | " \n", 801 | " \n", 802 | " \n", 803 | " \n", 804 | " \n", 805 | " \n", 806 | " \n", 807 | " \n", 808 | " \n", 809 | " \n", 810 | " \n", 811 | " \n", 812 | " \n", 813 | " \n", 814 | " \n", 815 | " \n", 816 | " \n", 817 | " \n", 818 | " \n", 819 | " \n", 820 | " \n", 821 | " \n", 822 | " \n", 823 | " \n", 824 | " \n", 825 | " \n", 826 | " \n", 827 | " \n", 828 | " \n", 829 | " \n", 830 | " \n", 831 | " \n", 832 | " \n", 833 | " \n", 834 | " \n", 835 | " \n", 836 | " \n", 837 | " \n", 838 | " \n", 839 | " \n", 840 | " \n", 841 | " \n", 842 | " \n", 843 | " \n", 844 | " \n", 845 | " \n", 846 | " \n", 847 | " \n", 848 | " \n", 849 | " \n", 850 | " \n", 851 | " \n", 852 | " \n", 853 | " \n", 854 | " \n", 855 | " \n", 856 | " \n", 857 | " \n", 858 | " \n", 859 | " \n", 860 | " \n", 861 | " \n", 862 | " \n", 863 | " \n", 864 | " \n", 865 | " \n", 866 | " \n", 867 | " \n", 868 | " \n", 869 | " \n", 870 | " \n", 871 | " \n", 872 | " \n", 873 | " \n", 874 | " \n", 875 | " \n", 876 | " \n", 877 | " \n", 878 | " \n", 879 | " \n", 880 | " \n", 881 | " \n", 882 | " \n", 883 | " \n", 884 | " \n", 885 | " \n", 886 | " \n", 887 | " \n", 888 | " \n", 889 | " \n", 890 | " \n", 891 | " \n", 892 | " \n", 893 | " \n", 894 | " \n", 895 | " \n", 896 | " \n", 897 | " \n", 898 | " \n", 899 | " \n", 900 | " \n", 901 | " \n", 902 | " \n", 903 | " \n", 904 | " \n", 905 | " \n", 906 | " \n", 907 | " \n", 908 | " \n", 909 | " \n", 910 | " \n", 911 | " \n", 912 | "
市場別股票代號股票名稱日期時間成交買進賣出漲跌張數昨收開盤最高最低類股別_ID類股_名稱
885TSE9941裕融2016-10-0413:3071.471.371.400.1171.071.371.371.471.26.0其他
886TSE9942茂順2016-10-0413:1886.985.986.401.637.085.385.886.985.26.0其他
887TSE9944新麗2016-10-0413:3024.224.224.50-0.3110.024.524.524.624.26.0其他
888TSE9945潤泰新2016-10-0414:3037.737.737.75-1.17229.038.838.638.637.66.0其他
889TSE9955佳龍2016-10-0413:3017.617.617.650.073.017.617.817.917.46.0其他
\n", 913 | "
" 914 | ], 915 | "text/plain": [ 916 | " 市場別 股票代號 股票名稱 日期 時間 成交 買進 賣出 漲跌 張數 昨收 \\\n", 917 | "885 TSE 9941 裕融 2016-10-04 13:30 71.4 71.3 71.40 0.1 171.0 71.3 \n", 918 | "886 TSE 9942 茂順 2016-10-04 13:18 86.9 85.9 86.40 1.6 37.0 85.3 \n", 919 | "887 TSE 9944 新麗 2016-10-04 13:30 24.2 24.2 24.50 -0.3 110.0 24.5 \n", 920 | "888 TSE 9945 潤泰新 2016-10-04 14:30 37.7 37.7 37.75 -1.1 7229.0 38.8 \n", 921 | "889 TSE 9955 佳龍 2016-10-04 13:30 17.6 17.6 17.65 0.0 73.0 17.6 \n", 922 | "\n", 923 | " 開盤 最高 最低 類股別_ID 類股_名稱 \n", 924 | "885 71.3 71.4 71.2 6.0 其他 \n", 925 | "886 85.8 86.9 85.2 6.0 其他 \n", 926 | "887 24.5 24.6 24.2 6.0 其他 \n", 927 | "888 38.6 38.6 37.6 6.0 其他 \n", 928 | "889 17.8 17.9 17.4 6.0 其他 " 929 | ] 930 | }, 931 | "execution_count": 16, 932 | "metadata": {}, 933 | "output_type": "execute_result" 934 | } 935 | ], 936 | "source": [ 937 | "mdf = df.merge(df_類股, left_on = '股票代號', right_on = '個股_代號', how = 'left') # merge\n", 938 | "mdf = mdf.drop(['市場別_ID', '個股_代號', '個股_名稱'], axis = 1) # drop 多於的欄位\n", 939 | "mdf.tail()" 940 | ] 941 | }, 942 | { 943 | "cell_type": "markdown", 944 | "metadata": {}, 945 | "source": [ 946 | "## GroupBy" 947 | ] 948 | }, 949 | { 950 | "cell_type": "code", 951 | "execution_count": 17, 952 | "metadata": { 953 | "collapsed": false 954 | }, 955 | "outputs": [ 956 | { 957 | "data": { 958 | "text/plain": [ 959 | "類股_名稱\n", 960 | "光電 69\n", 961 | "其他 46\n", 962 | "其它電子 32\n", 963 | "化工 25\n", 964 | "半導體 64\n", 965 | "塑膠 22\n", 966 | "憑證 7\n", 967 | "橡膠 10\n", 968 | "水泥 7\n", 969 | "汽車 6\n", 970 | "油電燃氣 8\n", 971 | "營建 48\n", 972 | "玻璃 4\n", 973 | "生技醫療 20\n", 974 | "紡織 46\n", 975 | "航運運輸 21\n", 976 | "觀光 13\n", 977 | "貿易百貨 11\n", 978 | "資訊服務 13\n", 979 | "通信網路 39\n", 980 | "造紙 7\n", 981 | "金融 33\n", 982 | "鋼鐵 30\n", 983 | "電器電纜 15\n", 984 | "電子通路 23\n", 985 | "電子零組件 81\n", 986 | "電機 43\n", 987 | "電腦週邊 60\n", 988 | "食品 21\n", 989 | "dtype: int64" 990 | ] 991 | }, 992 | "execution_count": 17, 993 | "metadata": {}, 994 | "output_type": "execute_result" 995 | } 996 | ], 997 | "source": [ 998 | "# 各類股有多少支個股\n", 999 | "mdf.groupby(['類股_名稱']).size().sort_index()" 1000 | ] 1001 | }, 1002 | { 1003 | "cell_type": "code", 1004 | "execution_count": 18, 1005 | "metadata": { 1006 | "collapsed": false 1007 | }, 1008 | "outputs": [ 1009 | { 1010 | "data": { 1011 | "text/plain": [ 1012 | "類股_名稱\n", 1013 | "光電 75.766667\n", 1014 | "其他 56.844130\n", 1015 | "其它電子 42.225000\n", 1016 | "化工 27.510400\n", 1017 | "半導體 50.935156\n", 1018 | "塑膠 28.105909\n", 1019 | "憑證 3.641429\n", 1020 | "橡膠 34.415000\n", 1021 | "水泥 19.055714\n", 1022 | "汽車 128.300000\n", 1023 | "油電燃氣 38.787500\n", 1024 | "營建 18.451250\n", 1025 | "玻璃 10.752500\n", 1026 | "生技醫療 54.405000\n", 1027 | "紡織 27.713696\n", 1028 | "航運運輸 15.499524\n", 1029 | "觀光 69.121538\n", 1030 | "貿易百貨 40.754545\n", 1031 | "資訊服務 35.570000\n", 1032 | "通信網路 38.973846\n", 1033 | "造紙 14.381429\n", 1034 | "金融 15.520606\n", 1035 | "鋼鐵 16.662333\n", 1036 | "電器電纜 11.322000\n", 1037 | "電子通路 34.553478\n", 1038 | "電子零組件 38.924321\n", 1039 | "電機 67.288837\n", 1040 | "電腦週邊 48.827833\n", 1041 | "食品 39.721905\n", 1042 | "Name: 成交, dtype: float64" 1043 | ] 1044 | }, 1045 | "execution_count": 18, 1046 | "metadata": {}, 1047 | "output_type": "execute_result" 1048 | } 1049 | ], 1050 | "source": [ 1051 | "# 各類股 平均股價\n", 1052 | "mdf.groupby(['類股_名稱'])['成交'].mean().sort_index()" 1053 | ] 1054 | } 1055 | ], 1056 | "metadata": { 1057 | "anaconda-cloud": {}, 1058 | "kernelspec": { 1059 | "display_name": "Python [default]", 1060 | "language": "python", 1061 | "name": "python3" 1062 | }, 1063 | "language_info": { 1064 | "codemirror_mode": { 1065 | "name": "ipython", 1066 | "version": 3 1067 | }, 1068 | "file_extension": ".py", 1069 | "mimetype": "text/x-python", 1070 | "name": "python", 1071 | "nbconvert_exporter": "python", 1072 | "pygments_lexer": "ipython3", 1073 | "version": "3.5.1" 1074 | } 1075 | }, 1076 | "nbformat": 4, 1077 | "nbformat_minor": 0 1078 | } 1079 | --------------------------------------------------------------------------------