├── .devcontainer ├── Dockerfile └── devcontainer.json ├── .github ├── CODEOWNERS ├── ISSUE_TEMPLATE.md ├── PULL_REQUEST_TEMPLATE.md └── workflows │ └── main.yml ├── .gitignore ├── .vscode └── settings.json ├── CONTRIBUTING.md ├── Ch01 ├── 01_01 │ └── code_cells.py ├── 01_02 │ └── extensions.py └── 01_03 │ └── markdown_cells.py ├── Ch02 ├── 02_01 │ └── np_overview.py ├── 02_02 │ └── arrays.py ├── 02_03 │ └── slicing.py ├── 02_04 │ └── boolean_indexing.py ├── 02_05 │ └── broadcasting.py ├── 02_06 │ └── operations.py ├── 02_07 │ └── ufuncs.py ├── challenge │ ├── flower.png │ └── square.py └── solution │ ├── flower.png │ └── square.py ├── Ch03 ├── 03_02 │ ├── load.py │ └── track.csv ├── 03_03 │ ├── parse_time.py │ └── track.csv ├── 03_04 │ ├── access.py │ └── track.csv ├── 03_05 │ ├── speed.py │ └── track.csv ├── 03_06 │ ├── box_plot.py │ └── track.csv ├── challenge │ ├── download_data.py │ └── mean_speed.py └── solution │ ├── download_data.py │ └── mean_speed.py ├── Ch04 ├── 04_01 │ ├── map.py │ ├── track.csv │ └── track.html ├── 04_02 │ ├── track.csv │ └── track.py ├── 04_03 │ ├── geo.py │ └── track.csv ├── challenge │ ├── height.py │ └── track.csv └── solution │ ├── height.py │ └── track.csv ├── Ch05 ├── 05_01 │ ├── download_data.py │ └── first_look.py ├── 05_02 │ ├── download_data.py │ └── load.py ├── 05_03 │ ├── categorical.py │ └── download_data.py ├── 05_04 │ ├── daily.py │ └── download_data.py ├── 05_05 │ ├── download_data.py │ └── hourly.py ├── 05_06 │ ├── download_data.py │ ├── download_weather.py │ └── weather.py ├── challenge │ ├── by_count.png │ ├── by_weekday.png │ ├── download_data.py │ └── tip.py └── solution │ ├── download_data.py │ └── tip.py ├── Ch06 ├── 06_02 │ └── regression.py ├── 06_03 │ └── split.py ├── 06_04 │ └── preprocess.py ├── 06_05 │ └── pipe.py ├── 06_06 │ └── save.py ├── challenge │ └── digits.py └── solution │ └── digits.py ├── Ch07 ├── 07_02 │ └── style.py ├── 07_03 │ └── pd_style.py ├── 07_04 │ ├── download_data.py │ └── pd_plot.py ├── 07_05 │ ├── download_data.py │ └── pd_mpl.py ├── 07_06 │ └── interactive.py ├── 07_07 │ ├── other.py │ └── track.csv ├── challenge │ ├── chart.png │ ├── download_data.py │ └── montly_close.py └── solution │ ├── download_data.py │ └── montly_close.py ├── Ch08 ├── 08_02 │ └── polysum.py ├── 08_03 │ └── digits.py ├── 08_04 │ ├── images.py │ └── sign.jpg ├── 08_05 │ └── nlp.py └── 08_06 │ ├── big.py │ └── download_data.py ├── Ch09 └── 09_05 │ ├── scale.py │ └── test_scale.py ├── LICENSE ├── NOTICE ├── README.md └── requirements.txt /.devcontainer/Dockerfile: -------------------------------------------------------------------------------- 1 | ARG VARIANT="3.10-bullseye" 2 | FROM mcr.microsoft.com/vscode/devcontainers/python:0-${VARIANT} 3 | 4 | ARG NODE_VERSION="none" 5 | RUN if [ "${NODE_VERSION}" != "none" ]; then su vscode -c "umask 0002 && . /usr/local/share/nvm/nvm.sh && nvm install ${NODE_VERSION} 2>&1"; fi 6 | 7 | RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \ 8 | && apt-get -y install --no-install-recommends graphviz libgl1 9 | 10 | RUN python -m pip install --upgrade pip 11 | COPY requirements.txt /tmp/pip-tmp/ 12 | RUN python -m pip --no-cache-dir install -r /tmp/pip-tmp/requirements.txt && rm -rf /tmp/pip-tmp 13 | RUN python -m spacy download en_core_web_sm 14 | -------------------------------------------------------------------------------- /.devcontainer/devcontainer.json: -------------------------------------------------------------------------------- 1 | { 2 | "build": { 3 | "dockerfile": "Dockerfile", 4 | "context": "..", 5 | "args": { 6 | "VARIANT": "3.10-bullseye", 7 | "NODE_VERSION": "lts/*" 8 | } 9 | }, 10 | "extensions": [ 11 | "GitHub.github-vscode-theme", 12 | "ms-python.python" 13 | ], 14 | "onCreateCommand" : "echo PS1='\"$ \"' >> ~/.bashrc" 15 | } 16 | -------------------------------------------------------------------------------- /.github/CODEOWNERS: -------------------------------------------------------------------------------- 1 | # Codeowners for these exercise files: 2 | # * (asterisk) denotes "all files and folders" 3 | # Example: * @producer @instructor 4 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | 7 | 8 | ## Issue Overview 9 | 10 | 11 | ## Describe your environment 12 | 13 | 14 | ## Steps to Reproduce 15 | 16 | 1. 17 | 2. 18 | 3. 19 | 4. 20 | 21 | ## Expected Behavior 22 | 23 | 24 | ## Current Behavior 25 | 26 | 27 | ## Possible Solution 28 | 29 | 30 | ## Screenshots / Video 31 | 32 | 33 | ## Related Issues 34 | 35 | -------------------------------------------------------------------------------- /.github/PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /.github/workflows/main.yml: -------------------------------------------------------------------------------- 1 | name: Copy To Branches 2 | on: 3 | workflow_dispatch: 4 | jobs: 5 | copy-to-branches: 6 | runs-on: ubuntu-latest 7 | steps: 8 | - uses: actions/checkout@v2 9 | with: 10 | fetch-depth: 0 11 | - name: Copy To Branches Action 12 | uses: planetoftheweb/copy-to-branches@v1.2 13 | env: 14 | key: main 15 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | *.pkl 2 | .DS_Store 3 | .tmp 4 | node_modules 5 | npm-debug.log 6 | taxi.csv 7 | taxi.parquet 8 | -------------------------------------------------------------------------------- /.vscode/settings.json: -------------------------------------------------------------------------------- 1 | { 2 | "editor.bracketPairColorization.enabled": true, 3 | "editor.cursorBlinking": "solid", 4 | "editor.fontFamily": "ui-monospace, Menlo, Monaco, 'Cascadia Mono', 'Segoe UI Mono', 'Roboto Mono', 'Oxygen Mono', 'Ubuntu Monospace', 'Source Code Pro', 'Fira Mono', 'Droid Sans Mono', 'Courier New', monospace", 5 | "editor.fontLigatures": false, 6 | "editor.fontSize": 22, 7 | "editor.formatOnPaste": true, 8 | "editor.formatOnSave": true, 9 | "editor.lineNumbers": "on", 10 | "editor.matchBrackets": "always", 11 | "editor.minimap.enabled": false, 12 | "editor.smoothScrolling": true, 13 | "editor.tabSize": 2, 14 | "editor.useTabStops": true, 15 | "emmet.triggerExpansionOnTab": true, 16 | "explorer.openEditors.visible": 0, 17 | "files.autoSave": "afterDelay", 18 | "screencastMode.onlyKeyboardShortcuts": true, 19 | "terminal.integrated.fontSize": 18, 20 | "workbench.activityBar.visible": true, 21 | "workbench.colorTheme": "Visual Studio Dark", 22 | "workbench.fontAliasing": "antialiased", 23 | "workbench.statusBar.visible": true 24 | } 25 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | 2 | Contribution Agreement 3 | ====================== 4 | 5 | This repository does not accept pull requests (PRs). All pull requests will be closed. 6 | 7 | However, if any contributions (through pull requests, issues, feedback or otherwise) are provided, as a contributor, you represent that the code you submit is your original work or that of your employer (in which case you represent you have the right to bind your employer). By submitting code (or otherwise providing feedback), you (and, if applicable, your employer) are licensing the submitted code (and/or feedback) to LinkedIn and the open source community subject to the BSD 2-Clause license. 8 | -------------------------------------------------------------------------------- /Ch01/01_01/code_cells.py: -------------------------------------------------------------------------------- 1 | # %% 2 | 40 + 2 3 | 4 | # %% 5 | "Hi" + " there" 6 | 7 | # %% 8 | import matplotlib.pyplot as plt 9 | import numpy as np 10 | 11 | xs = np.linspace(-6, 6, 1000) 12 | ys = np.sin(xs) 13 | plt.plot(xs, ys) 14 | -------------------------------------------------------------------------------- /Ch01/01_02/extensions.py: -------------------------------------------------------------------------------- 1 | #%% 2 | %pwd 3 | # %% 4 | from time import sleep 5 | 6 | %time sleep(0.1) 7 | # %% 8 | %%time 9 | for i in range(3): 10 | sleep(0.1) 11 | # %% 12 | !ls /var/log 13 | # %% 14 | dirname = '/var/log' 15 | files = !ls $dirname 16 | print(f'{len(files)} files at {dirname}') 17 | -------------------------------------------------------------------------------- /Ch01/01_03/markdown_cells.py: -------------------------------------------------------------------------------- 1 | # %% [markdown] 2 | # # Shopping List 3 | # - Cheese 4 | # - Bread 5 | # - Lettuce 6 | 7 | # %% [markdown] 8 | # [Python](https://python.org) 9 | # ![](https://j.mp/python-logo) 10 | 11 | # %% [markdown] 12 | # Euler's identity is $e^{i\pi} + 1 = 0$ 13 | # %% 14 | -------------------------------------------------------------------------------- /Ch02/02_01/np_overview.py: -------------------------------------------------------------------------------- 1 | # %% 2 | 2 ** 1000 3 | # %% 4 | import numpy as np 5 | 6 | np.int64(2) ** 1000 7 | # %% 8 | np.sin(27) 9 | # %% 10 | np.sin(np.array([1, 2, 3])) -------------------------------------------------------------------------------- /Ch02/02_02/arrays.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import numpy as np 3 | 4 | # %% 5 | arr = np.array([1, 2, 3]) 6 | arr 7 | # %% 8 | len(arr) 9 | 10 | # %% 11 | arr[1] 12 | 13 | # %% 14 | type(arr[1]) 15 | 16 | # %% 17 | arr.dtype 18 | 19 | # %% 20 | arr32 = np.array([1,2,3], dtype=np.int32) 21 | arr32.dtype 22 | 23 | # %% 24 | arr * arr 25 | 26 | # %% 27 | v1 = np.random.rand(1_000_000) 28 | v2 = np.random.rand(1_000_000) 29 | %time v1 * v2 30 | 31 | # %% 32 | arr @ arr 33 | # %% 34 | mat = np.array([ 35 | [1,2,3], 36 | [4,5,6], 37 | [7, 8, 9], 38 | ]) 39 | mat 40 | 41 | # %% 42 | v = np.arange(12) 43 | v 44 | 45 | # %% 46 | v.reshape((4, 3)) 47 | 48 | # %% 49 | mat = np.arange(12).reshape((4, 3)) 50 | mat 51 | 52 | # %% 53 | mat.shape 54 | 55 | # %% 56 | mat2 = mat.reshape((3, 4)) 57 | mat2[1, 1] = 1000 58 | mat 59 | 60 | # %% 61 | mat.T -------------------------------------------------------------------------------- /Ch02/02_03/slicing.py: -------------------------------------------------------------------------------- 1 | # %% 2 | nums = [1, 2, 3, 4, 5] 3 | nums[2:4] 4 | 5 | # %% 6 | import numpy as np 7 | 8 | v = np.arange(1, 6) 9 | v[2:4] 10 | 11 | # %% 12 | arr = np.arange(12).reshape(3, 4) 13 | arr 14 | 15 | # %% 16 | arr[0] 17 | 18 | # %% 19 | arr[1][1] 20 | 21 | # %% 22 | arr[1, 1] 23 | # %% 24 | arr[:, 1] 25 | 26 | # %% 27 | arr[:, 1].reshape((3, 1)) 28 | 29 | # %% 30 | arr[1:, 2:] 31 | 32 | # %% 33 | arr[1:, 2:] = 7 34 | arr 35 | 36 | # %% 37 | -------------------------------------------------------------------------------- /Ch02/02_04/boolean_indexing.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import numpy as np 3 | 4 | arr = np.arange(3) 5 | arr 6 | 7 | # %% 8 | arr[[True, False, True]] 9 | 10 | # %% 11 | arr >= 1 12 | 13 | # %% 14 | arr[arr >= 1] 15 | 16 | # %% 17 | arr = np.arange(10) 18 | 19 | arr[(arr>2) & (arr<7)] 20 | 21 | # %% 22 | arr[(arr>7) | (arr<2)] 23 | 24 | # %% 25 | arr[~(arr>7)] 26 | 27 | # %% 28 | values = np.random.normal(0, 10, 1_000) 29 | values[33] = 1038 30 | values[832] = -3423 31 | 32 | # %% 33 | mask = np.abs(values - values.mean()) > (2 * values.std()) 34 | values[mask] 35 | 36 | # %% 37 | values[mask] = values.mean() -------------------------------------------------------------------------------- /Ch02/02_05/broadcasting.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import numpy as np 3 | 4 | arr = np.arange(3) 5 | arr + 4 6 | 7 | # %% 8 | mat = np.arange(9).reshape((3,3)) 9 | vec = np.arange(3) 10 | 11 | mat + vec 12 | 13 | # %% 14 | v1 = np.arange(3) 15 | v2 = np.arange(3).reshape((3, 1)) 16 | v2 17 | 18 | # %% 19 | v1 + v2 -------------------------------------------------------------------------------- /Ch02/02_06/operations.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import numpy as np 3 | 4 | v = np.array([ 5 | [0, 1, 2], 6 | [3, 4, 5], 7 | [6, 7, 8], 8 | ]) 9 | v 10 | 11 | # %% 12 | v.T 13 | # %% 14 | v.any() 15 | 16 | # %% 17 | v.all() 18 | 19 | # %% 20 | if v: 21 | print('ok') 22 | # %% 23 | v.prod() 24 | 25 | # %% 26 | v.sum(axis=1) 27 | 28 | # %% 29 | v.sum(axis=0) 30 | 31 | # %% 32 | v1 = v.copy() 33 | v1[0,0] = -1 34 | v -------------------------------------------------------------------------------- /Ch02/02_07/ufuncs.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import numpy as np 3 | len(dir(np)) 4 | 5 | # %% 6 | np.sin(90) 7 | # %% 8 | v = np.arange(-3, 3) 9 | np.sin(v) 10 | 11 | # %% 12 | def relu(n): 13 | if n < 0: 14 | return 0.0 15 | return n 16 | 17 | relu(-2) 18 | 19 | # %% 20 | relu(v) 21 | # %% 22 | 23 | @np.vectorize 24 | def relu(n): 25 | if n < 0: 26 | return 0.0 27 | return n 28 | 29 | relu(v) 30 | # %% 31 | relu(-2) 32 | 33 | # %% 34 | relu(-2) - 7 35 | 36 | # %% 37 | nv = np.array([-1.0, np.nan, 1.0]) 38 | np.sin(nv) -------------------------------------------------------------------------------- /Ch02/challenge/flower.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LinkedInLearning/data-science-foundations-python-scientific-stack-3084641/cb2d7702e66ff3e80816d9f49844c80c18cfa0ba/Ch02/challenge/flower.png -------------------------------------------------------------------------------- /Ch02/challenge/square.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import matplotlib.pyplot as plt 3 | 4 | img = plt.imread('flower.png') 5 | img = img.copy() # make img writable 6 | plt.imshow(img) 7 | 8 | #%% 9 | type(img) 10 | # %% 11 | img.shape 12 | 13 | # %% 14 | # Draw a blue square around the flower 15 | # Top-left: 190x350 16 | # Bottom-right: 680x850 17 | # Line width: 5 -------------------------------------------------------------------------------- /Ch02/solution/flower.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LinkedInLearning/data-science-foundations-python-scientific-stack-3084641/cb2d7702e66ff3e80816d9f49844c80c18cfa0ba/Ch02/solution/flower.png -------------------------------------------------------------------------------- /Ch02/solution/square.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import matplotlib.pyplot as plt 3 | 4 | img = plt.imread('flower.png') 5 | img = img.copy() # make img writable 6 | plt.imshow(img) 7 | #%% 8 | type(img) 9 | # %% 10 | img.shape 11 | 12 | # %% 13 | # Draw a blue square around the flower 14 | # Top-left: 190x350 15 | # Bottom-right: 680x850 16 | # Line width: 5 17 | 18 | # first dimension is rows (y) 19 | tl_x, tl_y = 350, 190 20 | br_x, br_y = 850, 680 21 | width = 5 22 | 23 | color = [0, 0, 0xFF] # blue 24 | 25 | # Top line 26 | img[tl_x:tl_x+width, tl_y:br_y] = color 27 | # Bottom line 28 | img[br_x:br_x+width, tl_y:br_y] = color 29 | # Left line 30 | img[tl_x:br_x, tl_y:tl_y+width] = color 31 | # Right line 32 | img[tl_x:br_x, br_y-width:br_y] = color 33 | 34 | plt.imshow(img) -------------------------------------------------------------------------------- /Ch03/03_02/load.py: -------------------------------------------------------------------------------- 1 | # %% File size 2 | from pathlib import Path 3 | 4 | kb = 2**10 5 | 6 | csv_file = Path('track.csv') 7 | csv_file.stat().st_size / kb 8 | 9 | # %% 10 | !ls -lh $csv_file 11 | 12 | # %% First few lines & line count 13 | count = 0 14 | with csv_file.open() as fp: 15 | for lnum, line in enumerate(fp, 1): 16 | count += 1 17 | if lnum <= 5: 18 | print(line, end='') 19 | print(f'{count} lines') 20 | 21 | # %% First few lines 22 | !head -5 $csv_file 23 | 24 | # %% Line count 25 | !wc -l $csv_file 26 | 27 | # %% Load to data frame 28 | import pandas as pd 29 | 30 | df = pd.read_csv(csv_file) 31 | len(df) 32 | 33 | # %% 34 | df.columns 35 | # %% 36 | df.info() 37 | # %% 38 | df.describe() 39 | -------------------------------------------------------------------------------- /Ch03/03_02/track.csv: -------------------------------------------------------------------------------- 1 | time,lat,lng,height 2 | 2015-08-20 03:48:07.235,32.519585,35.015021,136.1999969482422 3 | 2015-08-20 03:48:24.734,32.519606,35.014954,126.5999984741211 4 | 2015-08-20 03:48:25.660,32.519612,35.014871,123.0 5 | 2015-08-20 03:48:26.819,32.519654,35.014824,120.5 6 | 2015-08-20 03:48:27.828,32.519689,35.014776,118.9000015258789 7 | 2015-08-20 03:48:29.720,32.519691,35.014704,119.9000015258789 8 | 2015-08-20 03:48:30.669,32.519734,35.014657,120.9000015258789 9 | 2015-08-20 03:48:33.793,32.519719,35.014563,121.6999969482422 10 | 2015-08-20 03:48:34.869,32.519694,35.014549,121.1999969482422 11 | 2015-08-20 03:48:37.708,32.519625,35.014515,121.6999969482422 12 | 2015-08-20 03:48:38.839,32.519599,35.014505,121.8000030517578 13 | 2015-08-20 03:48:41.980,32.519514,35.014481,122.5999984741211 14 | 2015-08-20 03:48:42.725,32.519486,35.014472,123.0 15 | 2015-08-20 03:48:45.896,32.519405,35.014439,122.6999969482422 16 | 2015-08-20 03:48:46.662,32.519379,35.014432,122.6999969482422 17 | 2015-08-20 03:48:49.829,32.519309,35.014414,122.6999969482422 18 | 2015-08-20 03:48:50.665,32.519287,35.0144,123.3000030517578 19 | 2015-08-20 03:48:53.692,32.519211,35.014372,122.3000030517578 20 | 2015-08-20 03:48:54.662,32.519187,35.014365,122.5999984741211 21 | 2015-08-20 03:48:58.869,32.519106,35.014337,122.0 22 | 2015-08-20 03:48:59.663,32.519084,35.014331,121.8000030517578 23 | 2015-08-20 03:49:03.861,32.519009,35.014303,122.9000015258789 24 | 2015-08-20 03:49:04.663,32.518989,35.014306,122.9000015258789 25 | 2015-08-20 03:49:07.742,32.518916,35.01434,123.0999984741211 26 | 2015-08-20 03:50:13.679,32.517636,35.013531,115.1999969482422 27 | 2015-08-20 03:50:16.767,32.517589,35.013606,114.3000030517578 28 | 2015-08-20 03:50:17.719,32.517574,35.01363,114.1999969482422 29 | 2015-08-20 03:50:20.748,32.517529,35.013705,113.8000030517578 30 | 2015-08-20 03:50:21.681,32.517512,35.01373,114.1999969482422 31 | 2015-08-20 03:50:24.735,32.517459,35.013809,114.0 32 | 2015-08-20 03:50:25.677,32.517442,35.013834,113.0999984741211 33 | 2015-08-20 03:50:28.738,32.517389,35.013903,112.5999984741211 34 | 2015-08-20 03:50:29.673,32.517373,35.013925,113.5 35 | 2015-08-20 03:50:32.816,32.517323,35.013991,113.5999984741211 36 | 2015-08-20 03:50:33.682,32.517306,35.014012,113.4000015258789 37 | 2015-08-20 03:50:36.749,32.517254,35.014075,114.5999984741211 38 | 2015-08-20 03:50:37.744,32.517236,35.014095,114.1999969482422 39 | 2015-08-20 03:50:40.735,32.517183,35.014148,115.5 40 | 2015-08-20 03:50:41.682,32.517164,35.014167,116.0 41 | 2015-08-20 03:50:44.757,32.517111,35.014235,116.5999984741211 42 | 2015-08-20 03:50:45.674,32.517095,35.014257,116.5999984741211 43 | 2015-08-20 03:50:48.716,32.517044,35.014332,115.3000030517578 44 | 2015-08-20 03:50:49.684,32.517026,35.014357,115.0 45 | 2015-08-20 03:50:52.757,32.516972,35.014431,114.9000015258789 46 | 2015-08-20 03:50:53.688,32.516954,35.014456,114.9000015258789 47 | 2015-08-20 03:50:56.782,32.516891,35.014519,115.6999969482422 48 | 2015-08-20 03:50:57.731,32.516868,35.014522,114.0 49 | 2015-08-20 03:51:00.870,32.516818,35.01447,113.5999984741211 50 | 2015-08-20 03:51:01.772,32.516803,35.014442,113.0 51 | 2015-08-20 03:51:04.744,32.516752,35.01436,112.0 52 | 2015-08-20 03:51:05.747,32.516734,35.014335,112.3000030517578 53 | 2015-08-20 03:51:08.831,32.516677,35.014257,110.9000015258789 54 | 2015-08-20 03:51:09.744,32.516657,35.014233,110.8000030517578 55 | 2015-08-20 03:51:12.743,32.516591,35.014176,108.6999969482422 56 | 2015-08-20 03:51:13.702,32.516568,35.014162,108.4000015258789 57 | 2015-08-20 03:51:16.777,32.516506,35.014103,109.1999969482422 58 | 2015-08-20 03:51:17.729,32.516486,35.014084,108.4000015258789 59 | 2015-08-20 03:51:20.733,32.516421,35.014026,108.5 60 | 2015-08-20 03:51:21.670,32.5164,35.014006,108.5999984741211 61 | 2015-08-20 03:51:24.691,32.516338,35.013941,107.5999984741211 62 | 2015-08-20 03:51:25.672,32.516318,35.01392,107.9000015258789 63 | 2015-08-20 03:51:28.745,32.516253,35.013869,108.0 64 | 2015-08-20 03:51:29.677,32.51623,35.013852,108.0 65 | 2015-08-20 03:51:32.707,32.516174,35.013795,105.5999984741211 66 | 2015-08-20 03:51:33.674,32.516156,35.013775,104.8000030517578 67 | 2015-08-20 03:51:36.758,32.516108,35.01371,105.3000030517578 68 | 2015-08-20 03:51:37.675,32.516089,35.01369,103.9000015258789 69 | 2015-08-20 03:51:40.733,32.516033,35.013633,103.6999969482422 70 | 2015-08-20 03:51:41.674,32.516014,35.013615,103.5 71 | 2015-08-20 03:51:44.744,32.51596,35.013559,102.8000030517578 72 | 2015-08-20 03:51:45.679,32.515943,35.01354,103.0 73 | 2015-08-20 03:51:48.760,32.515891,35.013482,102.0 74 | 2015-08-20 03:51:49.684,32.515873,35.013463,102.0 75 | 2015-08-20 03:51:52.709,32.515826,35.013403,102.0 76 | 2015-08-20 03:51:53.672,32.515812,35.013382,102.0 77 | 2015-08-20 03:51:56.894,32.515761,35.013325,101.8000030517578 78 | 2015-08-20 03:51:57.767,32.515743,35.013307,101.3000030517578 79 | 2015-08-20 03:52:00.768,32.515693,35.013246,100.5 80 | 2015-08-20 03:52:01.676,32.515675,35.013226,100.8000030517578 81 | 2015-08-20 03:52:04.866,32.515622,35.013162,101.0999984741211 82 | 2015-08-20 03:52:05.780,32.515605,35.013139,100.6999969482422 83 | 2015-08-20 03:52:08.753,32.51555,35.013074,99.1999969482422 84 | 2015-08-20 03:52:09.700,32.515531,35.013055,99.1999969482422 85 | 2015-08-20 03:52:12.855,32.515471,35.012994,99.3000030517578 86 | 2015-08-20 03:52:13.780,32.515451,35.012974,99.0 87 | 2015-08-20 03:52:16.775,32.515388,35.012912,99.6999969482422 88 | 2015-08-20 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04:11:48.862,32.511036,35.021326,99.6999969482422 526 | 2015-08-20 04:11:49.796,32.511016,35.021322,100.0999984741211 527 | 2015-08-20 04:11:53.868,32.510939,35.021304,99.4000015258789 528 | 2015-08-20 04:11:54.783,32.51092,35.021295,99.3000030517578 529 | 2015-08-20 04:11:58.880,32.510854,35.021241,99.5 530 | 2015-08-20 04:11:59.783,32.51084,35.021223,98.6999969482422 531 | 2015-08-20 04:12:03.871,32.510782,35.021152,97.3000030517578 532 | 2015-08-20 04:12:04.793,32.510766,35.021134,97.5999984741211 533 | 2015-08-20 04:12:08.851,32.510714,35.021052,96.5999984741211 534 | 2015-08-20 04:12:09.802,32.510703,35.02103,96.9000015258789 535 | 2015-08-20 04:12:13.871,32.510683,35.020927,96.0999984741211 536 | 2015-08-20 04:12:14.784,32.510684,35.020899,96.0999984741211 537 | 2015-08-20 04:12:17.844,32.510691,35.020811,96.5999984741211 538 | 2015-08-20 04:12:18.868,32.510695,35.02078,97.0 539 | 2015-08-20 04:12:21.929,32.51071,35.020689,96.6999969482422 540 | 2015-08-20 04:12:22.857,32.510717,35.02066,96.5999984741211 541 | 2015-08-20 04:12:25.926,32.51074,35.020582,96.0999984741211 542 | 2015-08-20 04:12:26.841,32.510747,35.020556,95.5999984741211 543 | 2015-08-20 04:12:30.906,32.510777,35.020461,94.3000030517578 544 | 2015-08-20 04:12:32.078,32.510786,35.020438,94.1999969482422 545 | 2015-08-20 04:12:35.899,32.510816,35.02034,92.4000015258789 546 | 2015-08-20 04:12:37.006,32.51082,35.020313,92.5999984741211 547 | 2015-08-20 04:12:39.957,32.510831,35.020222,93.9000015258789 548 | 2015-08-20 04:12:40.800,32.510836,35.020193,93.3000030517578 549 | 2015-08-20 04:12:43.876,32.510856,35.020111,93.5 550 | 2015-08-20 04:12:44.798,32.510863,35.020084,93.5 551 | 2015-08-20 04:12:47.876,32.510892,35.02001,92.9000015258789 552 | 2015-08-20 04:12:48.814,32.510902,35.019987,92.6999969482422 553 | 2015-08-20 04:12:52.978,32.510944,35.019899,92.3000030517578 554 | 2015-08-20 04:12:53.847,32.510955,35.019877,92.0 555 | 2015-08-20 04:12:57.829,32.510988,35.019784,92.1999969482422 556 | 2015-08-20 04:12:58.800,32.510993,35.019759,91.9000015258789 557 | 2015-08-20 04:13:01.913,32.511,35.019676,90.5999984741211 558 | 2015-08-20 04:13:03.025,32.511004,35.019648,90.5999984741211 559 | 2015-08-20 04:13:05.853,32.511023,35.019567,90.0 560 | 2015-08-20 04:13:06.809,32.511031,35.019541,89.5999984741211 561 | 2015-08-20 04:13:09.907,32.511058,35.01947,88.69999694824219 562 | 2015-08-20 04:13:11.796,32.511085,35.019434,88.5 563 | 2015-08-20 04:13:15.810,32.511152,35.019368,89.5999984741211 564 | 2015-08-20 04:13:16.782,32.51117,35.019353,89.0999984741211 565 | 2015-08-20 04:13:19.841,32.511213,35.019291,90.3000030517578 566 | 2015-08-20 04:13:20.799,32.511225,35.019267,91.4000015258789 567 | 2015-08-20 04:13:24.818,32.511276,35.019184,91.8000030517578 568 | 2015-08-20 04:13:25.969,32.511286,35.019163,92.0999984741211 569 | 2015-08-20 04:13:29.993,32.511319,35.01907,92.1999969482422 570 | 2015-08-20 04:13:30.782,32.51133,35.019048,92.6999969482422 571 | 2015-08-20 04:13:34.847,32.511367,35.018962,92.1999969482422 572 | 2015-08-20 04:13:35.784,32.511371,35.01894,91.9000015258789 573 | 2015-08-20 04:13:40.817,32.511413,35.01886,92.0999984741211 574 | 2015-08-20 04:13:41.783,32.511419,35.018845,92.0 575 | 2015-08-20 04:13:47.873,32.511452,35.018747,90.4000015258789 576 | 2015-08-20 04:13:48.828,32.511457,35.018728,91.0 577 | 2015-08-20 04:13:52.897,32.511486,35.018646,92.4000015258789 578 | 2015-08-20 04:13:53.799,32.511495,35.018627,91.9000015258789 579 | 2015-08-20 04:13:57.883,32.511541,35.018559,92.5 580 | 2015-08-20 04:13:58.794,32.511551,35.018542,91.8000030517578 581 | 2015-08-20 04:14:03.880,32.511598,35.01846,92.8000030517578 582 | 2015-08-20 04:14:04.928,32.511604,35.018444,92.5999984741211 583 | 2015-08-20 04:14:10.074,32.511663,35.018369,92.8000030517578 584 | 2015-08-20 04:14:10.882,32.511675,35.018355,92.4000015258789 585 | 2015-08-20 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04:14:59.818,32.512176,35.017623,96.0999984741211 601 | 2015-08-20 04:15:05.903,32.512235,35.017543,96.8000030517578 602 | 2015-08-20 04:15:06.857,32.512245,35.01753,97.0 603 | 2015-08-20 04:15:12.020,32.512297,35.017461,94.1999969482422 604 | 2015-08-20 04:15:12.817,32.512305,35.017448,93.6999969482422 605 | 2015-08-20 04:15:19.098,32.512358,35.017372,95.1999969482422 606 | 2015-08-20 04:15:19.947,32.512367,35.017358,94.9000015258789 607 | 2015-08-20 04:15:24.889,32.512411,35.017285,94.3000030517578 608 | 2015-08-20 04:15:25.969,32.512421,35.01727,95.0 609 | 2015-08-20 04:15:31.057,32.51248,35.017192,96.4000015258789 610 | 2015-08-20 04:15:31.836,32.512492,35.017175,96.1999969482422 611 | 2015-08-20 04:15:36.910,32.512551,35.017096,98.1999969482422 612 | 2015-08-20 04:15:37.812,32.512563,35.017081,99.0 613 | 2015-08-20 04:15:42.857,32.512622,35.017014,97.1999969482422 614 | 2015-08-20 04:15:43.904,32.512629,35.016998,96.9000015258789 615 | 2015-08-20 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04:16:31.960,32.513221,35.01702,106.5999984741211 632 | 2015-08-20 04:16:32.888,32.513232,35.017037,107.3000030517578 633 | 2015-08-20 04:16:36.966,32.513276,35.017107,106.1999969482422 634 | 2015-08-20 04:16:37.866,32.513287,35.017123,107.4000015258789 635 | 2015-08-20 04:16:43.057,32.51333,35.017208,108.8000030517578 636 | 2015-08-20 04:16:43.873,32.51334,35.017226,110.3000030517578 637 | 2015-08-20 04:16:47.848,32.513398,35.017278,109.5 638 | 2015-08-20 04:16:48.815,32.513411,35.017291,109.9000015258789 639 | 2015-08-20 04:16:52.875,32.513464,35.017348,111.1999969482422 640 | 2015-08-20 04:16:53.817,32.513477,35.017364,112.5 641 | 2015-08-20 04:16:57.999,32.513534,35.017427,113.4000015258789 642 | 2015-08-20 04:16:58.839,32.513548,35.017441,113.6999969482422 643 | 2015-08-20 04:17:03.026,32.513618,35.017485,113.0 644 | 2015-08-20 04:17:03.872,32.513639,35.01749,112.8000030517578 645 | 2015-08-20 04:17:07.843,32.513718,35.017455,113.5999984741211 646 | 2015-08-20 04:17:08.815,32.513731,35.017436,113.6999969482422 647 | 2015-08-20 04:17:11.930,32.51377,35.017373,114.0999984741211 648 | 2015-08-20 04:17:12.887,32.513784,35.017349,114.0 649 | 2015-08-20 04:17:15.874,32.513828,35.017284,114.5 650 | 2015-08-20 04:17:16.827,32.513843,35.017263,114.5999984741211 651 | 2015-08-20 04:17:19.920,32.513887,35.017201,114.4000015258789 652 | 2015-08-20 04:17:20.855,32.513897,35.017175,113.9000015258789 653 | 2015-08-20 04:17:24.052,32.51393,35.017102,113.5 654 | 2015-08-20 04:17:25.054,32.513935,35.017077,113.5999984741211 655 | 2015-08-20 04:17:27.916,32.51393,35.016994,113.9000015258789 656 | 2015-08-20 04:17:28.819,32.513929,35.016965,114.9000015258789 657 | 2015-08-20 04:17:31.911,32.513936,35.016881,114.9000015258789 658 | 2015-08-20 04:17:32.827,32.513941,35.016855,114.3000030517578 659 | 2015-08-20 04:17:36.970,32.513958,35.016764,113.5999984741211 660 | 2015-08-20 04:17:37.876,32.513959,35.016742,113.5999984741211 661 | 2015-08-20 04:17:41.925,32.513989,35.01666,113.5999984741211 662 | 2015-08-20 04:17:42.821,32.513999,35.016639,113.6999969482422 663 | 2015-08-20 04:17:46.931,32.514054,35.016579,111.9000015258789 664 | 2015-08-20 04:17:47.947,32.514065,35.016562,112.8000030517578 665 | 2015-08-20 04:17:51.892,32.514112,35.016492,113.6999969482422 666 | 2015-08-20 04:17:52.841,32.514126,35.016471,113.8000030517578 667 | 2015-08-20 04:17:56.908,32.51417,35.016384,114.1999969482422 668 | 2015-08-20 04:17:57.824,32.514178,35.016362,114.3000030517578 669 | 2015-08-20 04:18:01.921,32.51421,35.016274,114.3000030517578 670 | 2015-08-20 04:18:02.823,32.514221,35.016254,114.1999969482422 671 | 2015-08-20 04:18:06.896,32.514278,35.016184,114.8000030517578 672 | 2015-08-20 04:18:07.813,32.514296,35.016171,115.5999984741211 673 | 2015-08-20 04:18:10.895,32.514355,35.016146,115.5999984741211 674 | 2015-08-20 04:18:13.814,32.514421,35.016143,116.3000030517578 675 | 2015-08-20 04:18:17.869,32.514502,35.016162,117.5999984741211 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04:18:51.819,32.515146,35.015951,117.0999984741211 691 | 2015-08-20 04:18:54.007,32.515209,35.015942,117.0999984741211 692 | 2015-08-20 04:18:54.893,32.51524,35.015937,117.5 693 | 2015-08-20 04:18:57.939,32.515328,35.015943,118.5999984741211 694 | 2015-08-20 04:18:58.845,32.515358,35.015946,118.5 695 | 2015-08-20 04:19:01.992,32.515443,35.015964,118.3000030517578 696 | 2015-08-20 04:19:02.889,32.515462,35.015973,118.3000030517578 697 | 2015-08-20 04:19:06.990,32.515544,35.01601,118.6999969482422 698 | 2015-08-20 04:19:07.888,32.51556,35.016017,118.5999984741211 699 | 2015-08-20 04:19:11.886,32.515638,35.016042,118.5999984741211 700 | 2015-08-20 04:19:12.819,32.51566,35.016051,118.0 701 | 2015-08-20 04:19:16.911,32.515738,35.016069,117.9000015258789 702 | 2015-08-20 04:19:17.842,32.515757,35.016076,117.1999969482422 703 | 2015-08-20 04:19:21.908,32.515844,35.016087,116.4000015258789 704 | 2015-08-20 04:19:22.820,32.51587,35.016071,116.1999969482422 705 | 2015-08-20 04:19:25.934,32.515933,35.016016,116.0 706 | 2015-08-20 04:19:26.831,32.515953,35.015993,116.8000030517578 707 | 2015-08-20 04:19:29.879,32.516001,35.015918,116.5 708 | 2015-08-20 04:19:30.833,32.51602,35.015894,117.0 709 | 2015-08-20 04:19:34.152,32.516074,35.015828,116.5 710 | 2015-08-20 04:19:34.904,32.516091,35.015804,116.5 711 | 2015-08-20 04:19:37.851,32.516142,35.01573,116.5999984741211 712 | 2015-08-20 04:19:38.820,32.516157,35.015705,116.5 713 | 2015-08-20 04:19:41.881,32.516211,35.01564,115.4000015258789 714 | 2015-08-20 04:19:42.825,32.516225,35.015617,114.0999984741211 715 | 2015-08-20 04:19:45.963,32.516278,35.015551,113.5 716 | 2015-08-20 04:19:46.890,32.516301,35.015533,112.5 717 | 2015-08-20 04:19:49.987,32.516361,35.015468,112.0 718 | 2015-08-20 04:19:50.878,32.51638,35.015444,112.6999969482422 719 | 2015-08-20 04:19:53.937,32.516442,35.015369,112.5 720 | 2015-08-20 04:19:54.896,32.516462,35.015343,112.5999984741211 721 | 2015-08-20 04:19:58.048,32.516517,35.015264,112.3000030517578 722 | 2015-08-20 04:19:58.898,32.516537,35.015238,112.0999984741211 723 | 2015-08-20 04:20:01.965,32.516592,35.015157,110.0999984741211 724 | 2015-08-20 04:20:02.886,32.51661,35.015129,109.8000030517578 725 | 2015-08-20 04:20:05.959,32.516665,35.015048,107.6999969482422 726 | 2015-08-20 04:20:06.823,32.516683,35.015023,107.5 727 | 2015-08-20 04:20:09.926,32.516736,35.014943,107.5999984741211 728 | 2015-08-20 04:20:10.824,32.516754,35.014916,107.0999984741211 729 | 2015-08-20 04:20:14.040,32.5168,35.014835,106.0999984741211 730 | 2015-08-20 04:20:14.888,32.516818,35.01481,105.1999969482422 731 | 2015-08-20 04:20:17.897,32.516873,35.014737,106.4000015258789 732 | 2015-08-20 04:20:18.844,32.516889,35.014709,106.4000015258789 733 | 2015-08-20 04:20:21.996,32.516933,35.014617,103.5 734 | 2015-08-20 04:20:22.897,32.516945,35.014584,103.0999984741211 735 | 2015-08-20 04:20:24.905,32.516971,35.014518,103.8000030517578 736 | 2015-08-20 04:20:25.835,32.516984,35.014484,104.1999969482422 737 | 2015-08-20 04:20:28.982,32.51702,35.014387,104.8000030517578 738 | 2015-08-20 04:20:29.923,32.517035,35.014355,105.1999969482422 739 | 2015-08-20 04:20:32.863,32.517087,35.014279,102.9000015258789 740 | 2015-08-20 04:20:33.994,32.517098,35.014264,102.4000015258789 741 | 2015-08-20 04:20:42.329,32.517142,35.014212,103.4000015258789 742 | -------------------------------------------------------------------------------- /Ch03/03_03/parse_time.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | csv_file = 'track.csv' 5 | df = pd.read_csv(csv_file) 6 | df.dtypes 7 | # %% 8 | df = pd.read_csv(csv_file, parse_dates=['time']) 9 | df.dtypes 10 | 11 | # %% 12 | -------------------------------------------------------------------------------- /Ch03/03_03/track.csv: -------------------------------------------------------------------------------- 1 | time,lat,lng,height 2 | 2015-08-20 03:48:07.235,32.519585,35.015021,136.1999969482422 3 | 2015-08-20 03:48:24.734,32.519606,35.014954,126.5999984741211 4 | 2015-08-20 03:48:25.660,32.519612,35.014871,123.0 5 | 2015-08-20 03:48:26.819,32.519654,35.014824,120.5 6 | 2015-08-20 03:48:27.828,32.519689,35.014776,118.9000015258789 7 | 2015-08-20 03:48:29.720,32.519691,35.014704,119.9000015258789 8 | 2015-08-20 03:48:30.669,32.519734,35.014657,120.9000015258789 9 | 2015-08-20 03:48:33.793,32.519719,35.014563,121.6999969482422 10 | 2015-08-20 03:48:34.869,32.519694,35.014549,121.1999969482422 11 | 2015-08-20 03:48:37.708,32.519625,35.014515,121.6999969482422 12 | 2015-08-20 03:48:38.839,32.519599,35.014505,121.8000030517578 13 | 2015-08-20 03:48:41.980,32.519514,35.014481,122.5999984741211 14 | 2015-08-20 03:48:42.725,32.519486,35.014472,123.0 15 | 2015-08-20 03:48:45.896,32.519405,35.014439,122.6999969482422 16 | 2015-08-20 03:48:46.662,32.519379,35.014432,122.6999969482422 17 | 2015-08-20 03:48:49.829,32.519309,35.014414,122.6999969482422 18 | 2015-08-20 03:48:50.665,32.519287,35.0144,123.3000030517578 19 | 2015-08-20 03:48:53.692,32.519211,35.014372,122.3000030517578 20 | 2015-08-20 03:48:54.662,32.519187,35.014365,122.5999984741211 21 | 2015-08-20 03:48:58.869,32.519106,35.014337,122.0 22 | 2015-08-20 03:48:59.663,32.519084,35.014331,121.8000030517578 23 | 2015-08-20 03:49:03.861,32.519009,35.014303,122.9000015258789 24 | 2015-08-20 03:49:04.663,32.518989,35.014306,122.9000015258789 25 | 2015-08-20 03:49:07.742,32.518916,35.01434,123.0999984741211 26 | 2015-08-20 03:50:13.679,32.517636,35.013531,115.1999969482422 27 | 2015-08-20 03:50:16.767,32.517589,35.013606,114.3000030517578 28 | 2015-08-20 03:50:17.719,32.517574,35.01363,114.1999969482422 29 | 2015-08-20 03:50:20.748,32.517529,35.013705,113.8000030517578 30 | 2015-08-20 03:50:21.681,32.517512,35.01373,114.1999969482422 31 | 2015-08-20 03:50:24.735,32.517459,35.013809,114.0 32 | 2015-08-20 03:50:25.677,32.517442,35.013834,113.0999984741211 33 | 2015-08-20 03:50:28.738,32.517389,35.013903,112.5999984741211 34 | 2015-08-20 03:50:29.673,32.517373,35.013925,113.5 35 | 2015-08-20 03:50:32.816,32.517323,35.013991,113.5999984741211 36 | 2015-08-20 03:50:33.682,32.517306,35.014012,113.4000015258789 37 | 2015-08-20 03:50:36.749,32.517254,35.014075,114.5999984741211 38 | 2015-08-20 03:50:37.744,32.517236,35.014095,114.1999969482422 39 | 2015-08-20 03:50:40.735,32.517183,35.014148,115.5 40 | 2015-08-20 03:50:41.682,32.517164,35.014167,116.0 41 | 2015-08-20 03:50:44.757,32.517111,35.014235,116.5999984741211 42 | 2015-08-20 03:50:45.674,32.517095,35.014257,116.5999984741211 43 | 2015-08-20 03:50:48.716,32.517044,35.014332,115.3000030517578 44 | 2015-08-20 03:50:49.684,32.517026,35.014357,115.0 45 | 2015-08-20 03:50:52.757,32.516972,35.014431,114.9000015258789 46 | 2015-08-20 03:50:53.688,32.516954,35.014456,114.9000015258789 47 | 2015-08-20 03:50:56.782,32.516891,35.014519,115.6999969482422 48 | 2015-08-20 03:50:57.731,32.516868,35.014522,114.0 49 | 2015-08-20 03:51:00.870,32.516818,35.01447,113.5999984741211 50 | 2015-08-20 03:51:01.772,32.516803,35.014442,113.0 51 | 2015-08-20 03:51:04.744,32.516752,35.01436,112.0 52 | 2015-08-20 03:51:05.747,32.516734,35.014335,112.3000030517578 53 | 2015-08-20 03:51:08.831,32.516677,35.014257,110.9000015258789 54 | 2015-08-20 03:51:09.744,32.516657,35.014233,110.8000030517578 55 | 2015-08-20 03:51:12.743,32.516591,35.014176,108.6999969482422 56 | 2015-08-20 03:51:13.702,32.516568,35.014162,108.4000015258789 57 | 2015-08-20 03:51:16.777,32.516506,35.014103,109.1999969482422 58 | 2015-08-20 03:51:17.729,32.516486,35.014084,108.4000015258789 59 | 2015-08-20 03:51:20.733,32.516421,35.014026,108.5 60 | 2015-08-20 03:51:21.670,32.5164,35.014006,108.5999984741211 61 | 2015-08-20 03:51:24.691,32.516338,35.013941,107.5999984741211 62 | 2015-08-20 03:51:25.672,32.516318,35.01392,107.9000015258789 63 | 2015-08-20 03:51:28.745,32.516253,35.013869,108.0 64 | 2015-08-20 03:51:29.677,32.51623,35.013852,108.0 65 | 2015-08-20 03:51:32.707,32.516174,35.013795,105.5999984741211 66 | 2015-08-20 03:51:33.674,32.516156,35.013775,104.8000030517578 67 | 2015-08-20 03:51:36.758,32.516108,35.01371,105.3000030517578 68 | 2015-08-20 03:51:37.675,32.516089,35.01369,103.9000015258789 69 | 2015-08-20 03:51:40.733,32.516033,35.013633,103.6999969482422 70 | 2015-08-20 03:51:41.674,32.516014,35.013615,103.5 71 | 2015-08-20 03:51:44.744,32.51596,35.013559,102.8000030517578 72 | 2015-08-20 03:51:45.679,32.515943,35.01354,103.0 73 | 2015-08-20 03:51:48.760,32.515891,35.013482,102.0 74 | 2015-08-20 03:51:49.684,32.515873,35.013463,102.0 75 | 2015-08-20 03:51:52.709,32.515826,35.013403,102.0 76 | 2015-08-20 03:51:53.672,32.515812,35.013382,102.0 77 | 2015-08-20 03:51:56.894,32.515761,35.013325,101.8000030517578 78 | 2015-08-20 03:51:57.767,32.515743,35.013307,101.3000030517578 79 | 2015-08-20 03:52:00.768,32.515693,35.013246,100.5 80 | 2015-08-20 03:52:01.676,32.515675,35.013226,100.8000030517578 81 | 2015-08-20 03:52:04.866,32.515622,35.013162,101.0999984741211 82 | 2015-08-20 03:52:05.780,32.515605,35.013139,100.6999969482422 83 | 2015-08-20 03:52:08.753,32.51555,35.013074,99.1999969482422 84 | 2015-08-20 03:52:09.700,32.515531,35.013055,99.1999969482422 85 | 2015-08-20 03:52:12.855,32.515471,35.012994,99.3000030517578 86 | 2015-08-20 03:52:13.780,32.515451,35.012974,99.0 87 | 2015-08-20 03:52:16.775,32.515388,35.012912,99.6999969482422 88 | 2015-08-20 03:52:17.704,32.515368,35.012891,99.6999969482422 89 | 2015-08-20 03:52:20.907,32.515311,35.012826,99.0999984741211 90 | 2015-08-20 03:52:21.779,32.515291,35.012806,98.8000030517578 91 | 2015-08-20 03:52:24.813,32.515226,35.01276,98.5 92 | 2015-08-20 03:52:25.698,32.515203,35.012747,97.9000015258789 93 | 2015-08-20 03:52:28.748,32.515136,35.012709,97.4000015258789 94 | 2015-08-20 03:52:29.672,32.515113,35.012694,97.0 95 | 2015-08-20 03:52:32.719,32.515052,35.012636,96.3000030517578 96 | 2015-08-20 03:52:33.698,32.515034,35.012617,96.3000030517578 97 | 2015-08-20 03:52:37.790,32.514986,35.012537,96.0999984741211 98 | 2015-08-20 03:52:38.678,32.514964,35.012521,96.5999984741211 99 | 2015-08-20 03:52:41.752,32.514901,35.01246,95.1999969482422 100 | 2015-08-20 03:52:42.684,32.514882,35.012438,94.6999969482422 101 | 2015-08-20 03:52:45.758,32.514836,35.012371,94.3000030517578 102 | 2015-08-20 03:52:46.679,32.514813,35.012367,94.9000015258789 103 | 2015-08-20 03:52:49.764,32.514761,35.012418,95.0 104 | 2015-08-20 03:52:50.678,32.514746,35.012445,95.4000015258789 105 | 2015-08-20 03:52:54.323,32.514689,35.012516,96.4000015258789 106 | 2015-08-20 03:52:54.673,32.514671,35.01254,96.5 107 | 2015-08-20 03:52:57.937,32.514631,35.012615,98.0 108 | 2015-08-20 03:52:58.682,32.514619,35.012636,97.6999969482422 109 | 2015-08-20 03:53:02.766,32.514562,35.012717,99.0999984741211 110 | 2015-08-20 03:53:03.678,32.514545,35.012741,99.1999969482422 111 | 2015-08-20 03:53:05.884,32.514507,35.012782,99.4000015258789 112 | 2015-08-20 03:53:33.775,32.514134,35.013324,105.1999969482422 113 | 2015-08-20 03:53:36.808,32.514092,35.013393,105.8000030517578 114 | 2015-08-20 03:53:37.677,32.514077,35.013417,106.1999969482422 115 | 2015-08-20 03:53:40.734,32.514028,35.013493,106.3000030517578 116 | 2015-08-20 03:53:41.686,32.514011,35.013514,107.1999969482422 117 | 2015-08-20 03:53:44.713,32.513947,35.013472,106.6999969482422 118 | 2015-08-20 03:53:45.682,32.513928,35.013445,107.3000030517578 119 | 2015-08-20 03:53:49.290,32.513882,35.013363,103.9000015258789 120 | 2015-08-20 03:53:49.679,32.513869,35.013336,102.5999984741211 121 | 2015-08-20 03:53:52.831,32.513823,35.01327,105.4000015258789 122 | 2015-08-20 03:53:53.737,32.513807,35.013251,105.0999984741211 123 | 2015-08-20 03:53:56.820,32.513777,35.013178,105.1999969482422 124 | 2015-08-20 03:53:57.772,32.513769,35.013153,104.6999969482422 125 | 2015-08-20 03:54:00.947,32.513739,35.013078,104.1999969482422 126 | 2015-08-20 03:54:01.713,32.513726,35.013053,103.9000015258789 127 | 2015-08-20 03:54:04.796,32.513683,35.012981,102.8000030517578 128 | 2015-08-20 03:54:05.739,32.513666,35.012958,102.3000030517578 129 | 2015-08-20 03:54:08.837,32.513614,35.012891,100.4000015258789 130 | 2015-08-20 03:54:09.736,32.513598,35.012869,100.4000015258789 131 | 2015-08-20 03:54:12.795,32.513558,35.012791,100.5999984741211 132 | 2015-08-20 03:54:13.691,32.513546,35.012765,100.4000015258789 133 | 2015-08-20 03:54:17.425,32.513513,35.012687,99.6999969482422 134 | 2015-08-20 03:54:17.724,32.513502,35.01266,99.9000015258789 135 | 2015-08-20 03:54:20.759,32.513469,35.012578,102.0 136 | 2015-08-20 03:54:21.692,32.51346,35.01255,102.0999984741211 137 | 2015-08-20 03:54:24.813,32.513433,35.012471,101.5999984741211 138 | 2015-08-20 03:54:25.692,32.513426,35.012445,100.9000015258789 139 | 2015-08-20 03:54:28.783,32.513402,35.012367,100.0999984741211 140 | 2015-08-20 03:54:29.687,32.513394,35.012338,99.1999969482422 141 | 2015-08-20 03:54:32.827,32.513355,35.01226,99.5 142 | 2015-08-20 03:54:33.691,32.51334,35.012236,99.4000015258789 143 | 2015-08-20 03:54:36.774,32.513301,35.012159,98.3000030517578 144 | 2015-08-20 03:54:37.686,32.513288,35.012135,96.6999969482422 145 | 2015-08-20 03:54:40.787,32.513249,35.012063,95.1999969482422 146 | 2015-08-20 03:54:41.686,32.513237,35.012037,95.0 147 | 2015-08-20 03:54:44.769,32.513199,35.011961,94.6999969482422 148 | 2015-08-20 03:54:45.688,32.513184,35.011937,95.1999969482422 149 | 2015-08-20 03:54:48.745,32.51314,35.011868,93.3000030517578 150 | 2015-08-20 03:54:49.689,32.513126,35.011844,93.1999969482422 151 | 2015-08-20 03:54:52.757,32.513097,35.011766,92.5 152 | 2015-08-20 03:54:53.684,32.513088,35.011741,91.4000015258789 153 | 2015-08-20 03:54:57.781,32.513055,35.011644,91.1999969482422 154 | 2015-08-20 03:54:58.688,32.513048,35.011618,90.9000015258789 155 | 2015-08-20 03:55:01.904,32.513024,35.011538,90.6999969482422 156 | 2015-08-20 03:55:02.695,32.513016,35.01151,91.0 157 | 2015-08-20 03:55:05.766,32.512991,35.011432,90.0 158 | 2015-08-20 03:55:06.696,32.512982,35.011409,88.9000015258789 159 | 2015-08-20 03:55:10.766,32.512965,35.01131,88.0 160 | 2015-08-20 03:55:11.690,32.512962,35.011284,88.0999984741211 161 | 2015-08-20 03:55:14.787,32.512944,35.011205,87.9000015258789 162 | 2015-08-20 03:55:15.692,32.512936,35.011179,87.80000305175781 163 | 2015-08-20 03:55:18.789,32.512908,35.0111,86.80000305175781 164 | 2015-08-20 03:55:19.724,32.512897,35.011073,87.69999694824219 165 | 2015-08-20 03:55:22.747,32.512862,35.010991,88.0 166 | 2015-08-20 03:55:23.801,32.51285,35.010963,88.30000305175781 167 | 2015-08-20 03:55:26.824,32.51282,35.010873,88.0999984741211 168 | 2015-08-20 03:55:27.730,32.51281,35.010844,87.30000305175781 169 | 2015-08-20 03:55:30.791,32.512774,35.010765,87.4000015258789 170 | 2015-08-20 03:55:31.786,32.512762,35.010739,87.5999984741211 171 | 2015-08-20 03:55:34.837,32.512726,35.010661,86.80000305175781 172 | 2015-08-20 03:55:35.724,32.512714,35.010636,86.69999694824219 173 | 2015-08-20 03:55:38.786,32.512676,35.010562,87.19999694824219 174 | 2015-08-20 03:55:39.770,32.512664,35.010537,87.30000305175781 175 | 2015-08-20 03:55:42.826,32.512645,35.010455,87.0999984741211 176 | 2015-08-20 03:55:43.699,32.512642,35.010426,86.5999984741211 177 | 2015-08-20 03:55:46.771,32.512637,35.01034,85.0 178 | 2015-08-20 03:55:47.692,32.512636,35.010313,84.9000015258789 179 | 2015-08-20 03:55:51.779,32.512629,35.010212,84.9000015258789 180 | 2015-08-20 03:55:52.694,32.512625,35.010189,84.4000015258789 181 | 2015-08-20 03:55:56.849,32.512594,35.010093,82.9000015258789 182 | 2015-08-20 03:55:57.697,32.512584,35.010068,83.69999694824219 183 | 2015-08-20 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04:13:53.799,32.511495,35.018627,91.9000015258789 579 | 2015-08-20 04:13:57.883,32.511541,35.018559,92.5 580 | 2015-08-20 04:13:58.794,32.511551,35.018542,91.8000030517578 581 | 2015-08-20 04:14:03.880,32.511598,35.01846,92.8000030517578 582 | 2015-08-20 04:14:04.928,32.511604,35.018444,92.5999984741211 583 | 2015-08-20 04:14:10.074,32.511663,35.018369,92.8000030517578 584 | 2015-08-20 04:14:10.882,32.511675,35.018355,92.4000015258789 585 | 2015-08-20 04:14:16.882,32.511724,35.018267,90.4000015258789 586 | 2015-08-20 04:14:17.787,32.511738,35.018253,90.5 587 | 2015-08-20 04:14:21.900,32.511797,35.018196,89.0999984741211 588 | 2015-08-20 04:14:22.795,32.511806,35.018176,88.80000305175781 589 | 2015-08-20 04:14:27.035,32.511863,35.018103,93.5 590 | 2015-08-20 04:14:27.814,32.511876,35.018086,93.3000030517578 591 | 2015-08-20 04:14:32.930,32.51191,35.01799,91.3000030517578 592 | 2015-08-20 04:14:33.823,32.511919,35.017974,91.0 593 | 2015-08-20 04:14:39.152,32.511969,35.0179,93.0 594 | 2015-08-20 04:14:39.822,32.511981,35.017886,92.3000030517578 595 | 2015-08-20 04:14:45.935,32.512028,35.017796,94.1999969482422 596 | 2015-08-20 04:14:46.797,32.512037,35.017785,94.1999969482422 597 | 2015-08-20 04:14:52.855,32.5121,35.017713,93.9000015258789 598 | 2015-08-20 04:14:53.800,32.512113,35.017704,94.6999969482422 599 | 2015-08-20 04:14:58.953,32.512166,35.017636,96.3000030517578 600 | 2015-08-20 04:14:59.818,32.512176,35.017623,96.0999984741211 601 | 2015-08-20 04:15:05.903,32.512235,35.017543,96.8000030517578 602 | 2015-08-20 04:15:06.857,32.512245,35.01753,97.0 603 | 2015-08-20 04:15:12.020,32.512297,35.017461,94.1999969482422 604 | 2015-08-20 04:15:12.817,32.512305,35.017448,93.6999969482422 605 | 2015-08-20 04:15:19.098,32.512358,35.017372,95.1999969482422 606 | 2015-08-20 04:15:19.947,32.512367,35.017358,94.9000015258789 607 | 2015-08-20 04:15:24.889,32.512411,35.017285,94.3000030517578 608 | 2015-08-20 04:15:25.969,32.512421,35.01727,95.0 609 | 2015-08-20 04:15:31.057,32.51248,35.017192,96.4000015258789 610 | 2015-08-20 04:15:31.836,32.512492,35.017175,96.1999969482422 611 | 2015-08-20 04:15:36.910,32.512551,35.017096,98.1999969482422 612 | 2015-08-20 04:15:37.812,32.512563,35.017081,99.0 613 | 2015-08-20 04:15:42.857,32.512622,35.017014,97.1999969482422 614 | 2015-08-20 04:15:43.904,32.512629,35.016998,96.9000015258789 615 | 2015-08-20 04:15:48.929,32.512679,35.016924,98.0 616 | 2015-08-20 04:15:49.830,32.512686,35.016908,97.8000030517578 617 | 2015-08-20 04:15:54.946,32.512727,35.016819,98.6999969482422 618 | 2015-08-20 04:15:55.863,32.512735,35.0168,97.9000015258789 619 | 2015-08-20 04:15:59.939,32.512763,35.016714,97.0 620 | 2015-08-20 04:16:00.878,32.512769,35.016691,98.0 621 | 2015-08-20 04:16:05.942,32.512842,35.016637,99.8000030517578 622 | 2015-08-20 04:16:06.875,32.512859,35.016648,100.3000030517578 623 | 2015-08-20 04:16:10.956,32.512922,35.016715,100.0 624 | 2015-08-20 04:16:11.879,32.512936,35.016735,100.5 625 | 2015-08-20 04:16:15.900,32.512992,35.016803,101.5 626 | 2015-08-20 04:16:16.890,32.513005,35.016819,100.6999969482422 627 | 2015-08-20 04:16:20.894,32.51307,35.016871,102.4000015258789 628 | 2015-08-20 04:16:21.815,32.513084,35.016884,102.5 629 | 2015-08-20 04:16:27.117,32.513151,35.016949,105.4000015258789 630 | 2015-08-20 04:16:27.981,32.513166,35.016961,105.3000030517578 631 | 2015-08-20 04:16:31.960,32.513221,35.01702,106.5999984741211 632 | 2015-08-20 04:16:32.888,32.513232,35.017037,107.3000030517578 633 | 2015-08-20 04:16:36.966,32.513276,35.017107,106.1999969482422 634 | 2015-08-20 04:16:37.866,32.513287,35.017123,107.4000015258789 635 | 2015-08-20 04:16:43.057,32.51333,35.017208,108.8000030517578 636 | 2015-08-20 04:16:43.873,32.51334,35.017226,110.3000030517578 637 | 2015-08-20 04:16:47.848,32.513398,35.017278,109.5 638 | 2015-08-20 04:16:48.815,32.513411,35.017291,109.9000015258789 639 | 2015-08-20 04:16:52.875,32.513464,35.017348,111.1999969482422 640 | 2015-08-20 04:16:53.817,32.513477,35.017364,112.5 641 | 2015-08-20 04:16:57.999,32.513534,35.017427,113.4000015258789 642 | 2015-08-20 04:16:58.839,32.513548,35.017441,113.6999969482422 643 | 2015-08-20 04:17:03.026,32.513618,35.017485,113.0 644 | 2015-08-20 04:17:03.872,32.513639,35.01749,112.8000030517578 645 | 2015-08-20 04:17:07.843,32.513718,35.017455,113.5999984741211 646 | 2015-08-20 04:17:08.815,32.513731,35.017436,113.6999969482422 647 | 2015-08-20 04:17:11.930,32.51377,35.017373,114.0999984741211 648 | 2015-08-20 04:17:12.887,32.513784,35.017349,114.0 649 | 2015-08-20 04:17:15.874,32.513828,35.017284,114.5 650 | 2015-08-20 04:17:16.827,32.513843,35.017263,114.5999984741211 651 | 2015-08-20 04:17:19.920,32.513887,35.017201,114.4000015258789 652 | 2015-08-20 04:17:20.855,32.513897,35.017175,113.9000015258789 653 | 2015-08-20 04:17:24.052,32.51393,35.017102,113.5 654 | 2015-08-20 04:17:25.054,32.513935,35.017077,113.5999984741211 655 | 2015-08-20 04:17:27.916,32.51393,35.016994,113.9000015258789 656 | 2015-08-20 04:17:28.819,32.513929,35.016965,114.9000015258789 657 | 2015-08-20 04:17:31.911,32.513936,35.016881,114.9000015258789 658 | 2015-08-20 04:17:32.827,32.513941,35.016855,114.3000030517578 659 | 2015-08-20 04:17:36.970,32.513958,35.016764,113.5999984741211 660 | 2015-08-20 04:17:37.876,32.513959,35.016742,113.5999984741211 661 | 2015-08-20 04:17:41.925,32.513989,35.01666,113.5999984741211 662 | 2015-08-20 04:17:42.821,32.513999,35.016639,113.6999969482422 663 | 2015-08-20 04:17:46.931,32.514054,35.016579,111.9000015258789 664 | 2015-08-20 04:17:47.947,32.514065,35.016562,112.8000030517578 665 | 2015-08-20 04:17:51.892,32.514112,35.016492,113.6999969482422 666 | 2015-08-20 04:17:52.841,32.514126,35.016471,113.8000030517578 667 | 2015-08-20 04:17:56.908,32.51417,35.016384,114.1999969482422 668 | 2015-08-20 04:17:57.824,32.514178,35.016362,114.3000030517578 669 | 2015-08-20 04:18:01.921,32.51421,35.016274,114.3000030517578 670 | 2015-08-20 04:18:02.823,32.514221,35.016254,114.1999969482422 671 | 2015-08-20 04:18:06.896,32.514278,35.016184,114.8000030517578 672 | 2015-08-20 04:18:07.813,32.514296,35.016171,115.5999984741211 673 | 2015-08-20 04:18:10.895,32.514355,35.016146,115.5999984741211 674 | 2015-08-20 04:18:13.814,32.514421,35.016143,116.3000030517578 675 | 2015-08-20 04:18:17.869,32.514502,35.016162,117.5999984741211 676 | 2015-08-20 04:18:18.813,32.514522,35.016165,117.5 677 | 2015-08-20 04:18:22.862,32.514598,35.016182,117.4000015258789 678 | 2015-08-20 04:18:23.814,32.514616,35.016187,117.0999984741211 679 | 2015-08-20 04:18:27.863,32.514686,35.016171,116.6999969482422 680 | 2015-08-20 04:18:28.816,32.514703,35.016158,116.5999984741211 681 | 2015-08-20 04:18:32.870,32.514754,35.016085,116.5999984741211 682 | 2015-08-20 04:18:33.815,32.514765,35.01607,115.8000030517578 683 | 2015-08-20 04:18:37.847,32.514825,35.016018,117.3000030517578 684 | 2015-08-20 04:18:38.816,32.514843,35.016007,117.6999969482422 685 | 2015-08-20 04:18:42.882,32.514921,35.015984,117.5999984741211 686 | 2015-08-20 04:18:43.815,32.514942,35.015984,118.3000030517578 687 | 2015-08-20 04:18:47.897,32.51503,35.01597,118.3000030517578 688 | 2015-08-20 04:18:48.836,32.515053,35.01597,118.0999984741211 689 | 2015-08-20 04:18:50.877,32.515114,35.015958,116.4000015258789 690 | 2015-08-20 04:18:51.819,32.515146,35.015951,117.0999984741211 691 | 2015-08-20 04:18:54.007,32.515209,35.015942,117.0999984741211 692 | 2015-08-20 04:18:54.893,32.51524,35.015937,117.5 693 | 2015-08-20 04:18:57.939,32.515328,35.015943,118.5999984741211 694 | 2015-08-20 04:18:58.845,32.515358,35.015946,118.5 695 | 2015-08-20 04:19:01.992,32.515443,35.015964,118.3000030517578 696 | 2015-08-20 04:19:02.889,32.515462,35.015973,118.3000030517578 697 | 2015-08-20 04:19:06.990,32.515544,35.01601,118.6999969482422 698 | 2015-08-20 04:19:07.888,32.51556,35.016017,118.5999984741211 699 | 2015-08-20 04:19:11.886,32.515638,35.016042,118.5999984741211 700 | 2015-08-20 04:19:12.819,32.51566,35.016051,118.0 701 | 2015-08-20 04:19:16.911,32.515738,35.016069,117.9000015258789 702 | 2015-08-20 04:19:17.842,32.515757,35.016076,117.1999969482422 703 | 2015-08-20 04:19:21.908,32.515844,35.016087,116.4000015258789 704 | 2015-08-20 04:19:22.820,32.51587,35.016071,116.1999969482422 705 | 2015-08-20 04:19:25.934,32.515933,35.016016,116.0 706 | 2015-08-20 04:19:26.831,32.515953,35.015993,116.8000030517578 707 | 2015-08-20 04:19:29.879,32.516001,35.015918,116.5 708 | 2015-08-20 04:19:30.833,32.51602,35.015894,117.0 709 | 2015-08-20 04:19:34.152,32.516074,35.015828,116.5 710 | 2015-08-20 04:19:34.904,32.516091,35.015804,116.5 711 | 2015-08-20 04:19:37.851,32.516142,35.01573,116.5999984741211 712 | 2015-08-20 04:19:38.820,32.516157,35.015705,116.5 713 | 2015-08-20 04:19:41.881,32.516211,35.01564,115.4000015258789 714 | 2015-08-20 04:19:42.825,32.516225,35.015617,114.0999984741211 715 | 2015-08-20 04:19:45.963,32.516278,35.015551,113.5 716 | 2015-08-20 04:19:46.890,32.516301,35.015533,112.5 717 | 2015-08-20 04:19:49.987,32.516361,35.015468,112.0 718 | 2015-08-20 04:19:50.878,32.51638,35.015444,112.6999969482422 719 | 2015-08-20 04:19:53.937,32.516442,35.015369,112.5 720 | 2015-08-20 04:19:54.896,32.516462,35.015343,112.5999984741211 721 | 2015-08-20 04:19:58.048,32.516517,35.015264,112.3000030517578 722 | 2015-08-20 04:19:58.898,32.516537,35.015238,112.0999984741211 723 | 2015-08-20 04:20:01.965,32.516592,35.015157,110.0999984741211 724 | 2015-08-20 04:20:02.886,32.51661,35.015129,109.8000030517578 725 | 2015-08-20 04:20:05.959,32.516665,35.015048,107.6999969482422 726 | 2015-08-20 04:20:06.823,32.516683,35.015023,107.5 727 | 2015-08-20 04:20:09.926,32.516736,35.014943,107.5999984741211 728 | 2015-08-20 04:20:10.824,32.516754,35.014916,107.0999984741211 729 | 2015-08-20 04:20:14.040,32.5168,35.014835,106.0999984741211 730 | 2015-08-20 04:20:14.888,32.516818,35.01481,105.1999969482422 731 | 2015-08-20 04:20:17.897,32.516873,35.014737,106.4000015258789 732 | 2015-08-20 04:20:18.844,32.516889,35.014709,106.4000015258789 733 | 2015-08-20 04:20:21.996,32.516933,35.014617,103.5 734 | 2015-08-20 04:20:22.897,32.516945,35.014584,103.0999984741211 735 | 2015-08-20 04:20:24.905,32.516971,35.014518,103.8000030517578 736 | 2015-08-20 04:20:25.835,32.516984,35.014484,104.1999969482422 737 | 2015-08-20 04:20:28.982,32.51702,35.014387,104.8000030517578 738 | 2015-08-20 04:20:29.923,32.517035,35.014355,105.1999969482422 739 | 2015-08-20 04:20:32.863,32.517087,35.014279,102.9000015258789 740 | 2015-08-20 04:20:33.994,32.517098,35.014264,102.4000015258789 741 | 2015-08-20 04:20:42.329,32.517142,35.014212,103.4000015258789 742 | -------------------------------------------------------------------------------- /Ch03/03_04/access.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | csv_file = 'track.csv' 5 | df = pd.read_csv(csv_file, parse_dates=['time']) 6 | 7 | # %% 8 | df['lat'] 9 | 10 | # %% 11 | df.lat # Don't do this! 12 | 13 | # %% 14 | df[['lat', 'lng']] 15 | 16 | # %% 17 | df['lat'][0] 18 | 19 | # %% 20 | df.loc[0] 21 | 22 | # %% 23 | df.loc[2:7] 24 | 25 | # %% 26 | df[['lat', 'lng']][2:7] 27 | 28 | # %% 29 | 30 | df.index 31 | # %% 32 | import numpy as np 33 | 34 | df1 = pd.DataFrame( 35 | np.arange(10).reshape(5, 2), 36 | columns=['x', 'y'], 37 | index=['a', 'b', 'c', 'd', 'e'], 38 | ) 39 | df1 40 | 41 | # %% 42 | df1.loc[0] 43 | 44 | # %% 45 | df1.loc['a'] 46 | 47 | # %% 48 | df1.loc['a':'d'] 49 | 50 | # %% 51 | df1.iloc[0] 52 | 53 | # %% 54 | df.index 55 | 56 | # %% 57 | df.index = df['time'] 58 | df.index 59 | 60 | # %% 61 | df.loc[0] 62 | 63 | # %% 64 | df.loc['2015-08-20 03:48:34'] 65 | 66 | # %% 67 | 68 | df.loc['2015-08-20 03:48'] -------------------------------------------------------------------------------- /Ch03/03_04/track.csv: -------------------------------------------------------------------------------- 1 | time,lat,lng,height 2 | 2015-08-20 03:48:07.235,32.519585,35.015021,136.1999969482422 3 | 2015-08-20 03:48:24.734,32.519606,35.014954,126.5999984741211 4 | 2015-08-20 03:48:25.660,32.519612,35.014871,123.0 5 | 2015-08-20 03:48:26.819,32.519654,35.014824,120.5 6 | 2015-08-20 03:48:27.828,32.519689,35.014776,118.9000015258789 7 | 2015-08-20 03:48:29.720,32.519691,35.014704,119.9000015258789 8 | 2015-08-20 03:48:30.669,32.519734,35.014657,120.9000015258789 9 | 2015-08-20 03:48:33.793,32.519719,35.014563,121.6999969482422 10 | 2015-08-20 03:48:34.869,32.519694,35.014549,121.1999969482422 11 | 2015-08-20 03:48:37.708,32.519625,35.014515,121.6999969482422 12 | 2015-08-20 03:48:38.839,32.519599,35.014505,121.8000030517578 13 | 2015-08-20 03:48:41.980,32.519514,35.014481,122.5999984741211 14 | 2015-08-20 03:48:42.725,32.519486,35.014472,123.0 15 | 2015-08-20 03:48:45.896,32.519405,35.014439,122.6999969482422 16 | 2015-08-20 03:48:46.662,32.519379,35.014432,122.6999969482422 17 | 2015-08-20 03:48:49.829,32.519309,35.014414,122.6999969482422 18 | 2015-08-20 03:48:50.665,32.519287,35.0144,123.3000030517578 19 | 2015-08-20 03:48:53.692,32.519211,35.014372,122.3000030517578 20 | 2015-08-20 03:48:54.662,32.519187,35.014365,122.5999984741211 21 | 2015-08-20 03:48:58.869,32.519106,35.014337,122.0 22 | 2015-08-20 03:48:59.663,32.519084,35.014331,121.8000030517578 23 | 2015-08-20 03:49:03.861,32.519009,35.014303,122.9000015258789 24 | 2015-08-20 03:49:04.663,32.518989,35.014306,122.9000015258789 25 | 2015-08-20 03:49:07.742,32.518916,35.01434,123.0999984741211 26 | 2015-08-20 03:50:13.679,32.517636,35.013531,115.1999969482422 27 | 2015-08-20 03:50:16.767,32.517589,35.013606,114.3000030517578 28 | 2015-08-20 03:50:17.719,32.517574,35.01363,114.1999969482422 29 | 2015-08-20 03:50:20.748,32.517529,35.013705,113.8000030517578 30 | 2015-08-20 03:50:21.681,32.517512,35.01373,114.1999969482422 31 | 2015-08-20 03:50:24.735,32.517459,35.013809,114.0 32 | 2015-08-20 03:50:25.677,32.517442,35.013834,113.0999984741211 33 | 2015-08-20 03:50:28.738,32.517389,35.013903,112.5999984741211 34 | 2015-08-20 03:50:29.673,32.517373,35.013925,113.5 35 | 2015-08-20 03:50:32.816,32.517323,35.013991,113.5999984741211 36 | 2015-08-20 03:50:33.682,32.517306,35.014012,113.4000015258789 37 | 2015-08-20 03:50:36.749,32.517254,35.014075,114.5999984741211 38 | 2015-08-20 03:50:37.744,32.517236,35.014095,114.1999969482422 39 | 2015-08-20 03:50:40.735,32.517183,35.014148,115.5 40 | 2015-08-20 03:50:41.682,32.517164,35.014167,116.0 41 | 2015-08-20 03:50:44.757,32.517111,35.014235,116.5999984741211 42 | 2015-08-20 03:50:45.674,32.517095,35.014257,116.5999984741211 43 | 2015-08-20 03:50:48.716,32.517044,35.014332,115.3000030517578 44 | 2015-08-20 03:50:49.684,32.517026,35.014357,115.0 45 | 2015-08-20 03:50:52.757,32.516972,35.014431,114.9000015258789 46 | 2015-08-20 03:50:53.688,32.516954,35.014456,114.9000015258789 47 | 2015-08-20 03:50:56.782,32.516891,35.014519,115.6999969482422 48 | 2015-08-20 03:50:57.731,32.516868,35.014522,114.0 49 | 2015-08-20 03:51:00.870,32.516818,35.01447,113.5999984741211 50 | 2015-08-20 03:51:01.772,32.516803,35.014442,113.0 51 | 2015-08-20 03:51:04.744,32.516752,35.01436,112.0 52 | 2015-08-20 03:51:05.747,32.516734,35.014335,112.3000030517578 53 | 2015-08-20 03:51:08.831,32.516677,35.014257,110.9000015258789 54 | 2015-08-20 03:51:09.744,32.516657,35.014233,110.8000030517578 55 | 2015-08-20 03:51:12.743,32.516591,35.014176,108.6999969482422 56 | 2015-08-20 03:51:13.702,32.516568,35.014162,108.4000015258789 57 | 2015-08-20 03:51:16.777,32.516506,35.014103,109.1999969482422 58 | 2015-08-20 03:51:17.729,32.516486,35.014084,108.4000015258789 59 | 2015-08-20 03:51:20.733,32.516421,35.014026,108.5 60 | 2015-08-20 03:51:21.670,32.5164,35.014006,108.5999984741211 61 | 2015-08-20 03:51:24.691,32.516338,35.013941,107.5999984741211 62 | 2015-08-20 03:51:25.672,32.516318,35.01392,107.9000015258789 63 | 2015-08-20 03:51:28.745,32.516253,35.013869,108.0 64 | 2015-08-20 03:51:29.677,32.51623,35.013852,108.0 65 | 2015-08-20 03:51:32.707,32.516174,35.013795,105.5999984741211 66 | 2015-08-20 03:51:33.674,32.516156,35.013775,104.8000030517578 67 | 2015-08-20 03:51:36.758,32.516108,35.01371,105.3000030517578 68 | 2015-08-20 03:51:37.675,32.516089,35.01369,103.9000015258789 69 | 2015-08-20 03:51:40.733,32.516033,35.013633,103.6999969482422 70 | 2015-08-20 03:51:41.674,32.516014,35.013615,103.5 71 | 2015-08-20 03:51:44.744,32.51596,35.013559,102.8000030517578 72 | 2015-08-20 03:51:45.679,32.515943,35.01354,103.0 73 | 2015-08-20 03:51:48.760,32.515891,35.013482,102.0 74 | 2015-08-20 03:51:49.684,32.515873,35.013463,102.0 75 | 2015-08-20 03:51:52.709,32.515826,35.013403,102.0 76 | 2015-08-20 03:51:53.672,32.515812,35.013382,102.0 77 | 2015-08-20 03:51:56.894,32.515761,35.013325,101.8000030517578 78 | 2015-08-20 03:51:57.767,32.515743,35.013307,101.3000030517578 79 | 2015-08-20 03:52:00.768,32.515693,35.013246,100.5 80 | 2015-08-20 03:52:01.676,32.515675,35.013226,100.8000030517578 81 | 2015-08-20 03:52:04.866,32.515622,35.013162,101.0999984741211 82 | 2015-08-20 03:52:05.780,32.515605,35.013139,100.6999969482422 83 | 2015-08-20 03:52:08.753,32.51555,35.013074,99.1999969482422 84 | 2015-08-20 03:52:09.700,32.515531,35.013055,99.1999969482422 85 | 2015-08-20 03:52:12.855,32.515471,35.012994,99.3000030517578 86 | 2015-08-20 03:52:13.780,32.515451,35.012974,99.0 87 | 2015-08-20 03:52:16.775,32.515388,35.012912,99.6999969482422 88 | 2015-08-20 03:52:17.704,32.515368,35.012891,99.6999969482422 89 | 2015-08-20 03:52:20.907,32.515311,35.012826,99.0999984741211 90 | 2015-08-20 03:52:21.779,32.515291,35.012806,98.8000030517578 91 | 2015-08-20 03:52:24.813,32.515226,35.01276,98.5 92 | 2015-08-20 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04:09:59.850,32.511022,35.022282,97.5 486 | 2015-08-20 04:10:00.776,32.511032,35.0223,97.5999984741211 487 | 2015-08-20 04:10:05.856,32.511085,35.022376,98.9000015258789 488 | 2015-08-20 04:10:06.766,32.511094,35.022391,98.6999969482422 489 | 2015-08-20 04:10:12.468,32.511146,35.022471,99.8000030517578 490 | 2015-08-20 04:10:12.830,32.511155,35.022488,99.9000015258789 491 | 2015-08-20 04:10:17.817,32.511197,35.022577,99.6999969482422 492 | 2015-08-20 04:10:18.783,32.511205,35.022595,99.5999984741211 493 | 2015-08-20 04:10:23.827,32.511237,35.022677,100.6999969482422 494 | 2015-08-20 04:10:24.781,32.511247,35.02269,100.4000015258789 495 | 2015-08-20 04:10:29.980,32.511298,35.022761,101.8000030517578 496 | 2015-08-20 04:10:30.838,32.511309,35.022774,102.3000030517578 497 | 2015-08-20 04:10:35.889,32.51139,35.022733,103.3000030517578 498 | 2015-08-20 04:10:36.783,32.511398,35.022713,103.3000030517578 499 | 2015-08-20 04:10:40.836,32.511421,35.022627,99.9000015258789 500 | 2015-08-20 04:10:41.777,32.511418,35.0226,100.0999984741211 501 | 2015-08-20 04:10:45.853,32.511412,35.022497,99.5999984741211 502 | 2015-08-20 04:10:46.785,32.511417,35.022477,99.6999969482422 503 | 2015-08-20 04:10:51.857,32.511476,35.022398,103.5 504 | 2015-08-20 04:10:52.769,32.511487,35.022382,103.3000030517578 505 | 2015-08-20 04:10:57.843,32.511493,35.022287,100.6999969482422 506 | 2015-08-20 04:10:58.777,32.511486,35.022269,100.4000015258789 507 | 2015-08-20 04:11:02.933,32.511446,35.022192,100.6999969482422 508 | 2015-08-20 04:11:03.781,32.511434,35.022173,101.5 509 | 2015-08-20 04:11:07.870,32.511387,35.022099,103.8000030517578 510 | 2015-08-20 04:11:08.771,32.511378,35.022078,104.3000030517578 511 | 2015-08-20 04:11:12.832,32.511372,35.02199,105.5 512 | 2015-08-20 04:11:13.785,32.511375,35.021969,104.4000015258789 513 | 2015-08-20 04:11:18.859,32.511364,35.021865,104.4000015258789 514 | 2015-08-20 04:11:19.774,32.511358,35.021844,104.3000030517578 515 | 2015-08-20 04:11:23.916,32.511348,35.021755,104.3000030517578 516 | 2015-08-20 04:11:24.856,32.511341,35.021733,104.5999984741211 517 | 2015-08-20 04:11:28.859,32.511297,35.021653,103.5999984741211 518 | 2015-08-20 04:11:29.785,32.511285,35.021634,103.5999984741211 519 | 2015-08-20 04:11:33.916,32.511249,35.021544,102.6999969482422 520 | 2015-08-20 04:11:34.777,32.511242,35.021523,102.0 521 | 2015-08-20 04:11:38.845,32.511193,35.021447,101.6999969482422 522 | 2015-08-20 04:11:39.776,32.511181,35.02143,101.9000015258789 523 | 2015-08-20 04:11:43.854,32.511127,35.021367,101.4000015258789 524 | 2015-08-20 04:11:44.781,32.511111,35.021355,100.6999969482422 525 | 2015-08-20 04:11:48.862,32.511036,35.021326,99.6999969482422 526 | 2015-08-20 04:11:49.796,32.511016,35.021322,100.0999984741211 527 | 2015-08-20 04:11:53.868,32.510939,35.021304,99.4000015258789 528 | 2015-08-20 04:11:54.783,32.51092,35.021295,99.3000030517578 529 | 2015-08-20 04:11:58.880,32.510854,35.021241,99.5 530 | 2015-08-20 04:11:59.783,32.51084,35.021223,98.6999969482422 531 | 2015-08-20 04:12:03.871,32.510782,35.021152,97.3000030517578 532 | 2015-08-20 04:12:04.793,32.510766,35.021134,97.5999984741211 533 | 2015-08-20 04:12:08.851,32.510714,35.021052,96.5999984741211 534 | 2015-08-20 04:12:09.802,32.510703,35.02103,96.9000015258789 535 | 2015-08-20 04:12:13.871,32.510683,35.020927,96.0999984741211 536 | 2015-08-20 04:12:14.784,32.510684,35.020899,96.0999984741211 537 | 2015-08-20 04:12:17.844,32.510691,35.020811,96.5999984741211 538 | 2015-08-20 04:12:18.868,32.510695,35.02078,97.0 539 | 2015-08-20 04:12:21.929,32.51071,35.020689,96.6999969482422 540 | 2015-08-20 04:12:22.857,32.510717,35.02066,96.5999984741211 541 | 2015-08-20 04:12:25.926,32.51074,35.020582,96.0999984741211 542 | 2015-08-20 04:12:26.841,32.510747,35.020556,95.5999984741211 543 | 2015-08-20 04:12:30.906,32.510777,35.020461,94.3000030517578 544 | 2015-08-20 04:12:32.078,32.510786,35.020438,94.1999969482422 545 | 2015-08-20 04:12:35.899,32.510816,35.02034,92.4000015258789 546 | 2015-08-20 04:12:37.006,32.51082,35.020313,92.5999984741211 547 | 2015-08-20 04:12:39.957,32.510831,35.020222,93.9000015258789 548 | 2015-08-20 04:12:40.800,32.510836,35.020193,93.3000030517578 549 | 2015-08-20 04:12:43.876,32.510856,35.020111,93.5 550 | 2015-08-20 04:12:44.798,32.510863,35.020084,93.5 551 | 2015-08-20 04:12:47.876,32.510892,35.02001,92.9000015258789 552 | 2015-08-20 04:12:48.814,32.510902,35.019987,92.6999969482422 553 | 2015-08-20 04:12:52.978,32.510944,35.019899,92.3000030517578 554 | 2015-08-20 04:12:53.847,32.510955,35.019877,92.0 555 | 2015-08-20 04:12:57.829,32.510988,35.019784,92.1999969482422 556 | 2015-08-20 04:12:58.800,32.510993,35.019759,91.9000015258789 557 | 2015-08-20 04:13:01.913,32.511,35.019676,90.5999984741211 558 | 2015-08-20 04:13:03.025,32.511004,35.019648,90.5999984741211 559 | 2015-08-20 04:13:05.853,32.511023,35.019567,90.0 560 | 2015-08-20 04:13:06.809,32.511031,35.019541,89.5999984741211 561 | 2015-08-20 04:13:09.907,32.511058,35.01947,88.69999694824219 562 | 2015-08-20 04:13:11.796,32.511085,35.019434,88.5 563 | 2015-08-20 04:13:15.810,32.511152,35.019368,89.5999984741211 564 | 2015-08-20 04:13:16.782,32.51117,35.019353,89.0999984741211 565 | 2015-08-20 04:13:19.841,32.511213,35.019291,90.3000030517578 566 | 2015-08-20 04:13:20.799,32.511225,35.019267,91.4000015258789 567 | 2015-08-20 04:13:24.818,32.511276,35.019184,91.8000030517578 568 | 2015-08-20 04:13:25.969,32.511286,35.019163,92.0999984741211 569 | 2015-08-20 04:13:29.993,32.511319,35.01907,92.1999969482422 570 | 2015-08-20 04:13:30.782,32.51133,35.019048,92.6999969482422 571 | 2015-08-20 04:13:34.847,32.511367,35.018962,92.1999969482422 572 | 2015-08-20 04:13:35.784,32.511371,35.01894,91.9000015258789 573 | 2015-08-20 04:13:40.817,32.511413,35.01886,92.0999984741211 574 | 2015-08-20 04:13:41.783,32.511419,35.018845,92.0 575 | 2015-08-20 04:13:47.873,32.511452,35.018747,90.4000015258789 576 | 2015-08-20 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04:14:32.930,32.51191,35.01799,91.3000030517578 592 | 2015-08-20 04:14:33.823,32.511919,35.017974,91.0 593 | 2015-08-20 04:14:39.152,32.511969,35.0179,93.0 594 | 2015-08-20 04:14:39.822,32.511981,35.017886,92.3000030517578 595 | 2015-08-20 04:14:45.935,32.512028,35.017796,94.1999969482422 596 | 2015-08-20 04:14:46.797,32.512037,35.017785,94.1999969482422 597 | 2015-08-20 04:14:52.855,32.5121,35.017713,93.9000015258789 598 | 2015-08-20 04:14:53.800,32.512113,35.017704,94.6999969482422 599 | 2015-08-20 04:14:58.953,32.512166,35.017636,96.3000030517578 600 | 2015-08-20 04:14:59.818,32.512176,35.017623,96.0999984741211 601 | 2015-08-20 04:15:05.903,32.512235,35.017543,96.8000030517578 602 | 2015-08-20 04:15:06.857,32.512245,35.01753,97.0 603 | 2015-08-20 04:15:12.020,32.512297,35.017461,94.1999969482422 604 | 2015-08-20 04:15:12.817,32.512305,35.017448,93.6999969482422 605 | 2015-08-20 04:15:19.098,32.512358,35.017372,95.1999969482422 606 | 2015-08-20 04:15:19.947,32.512367,35.017358,94.9000015258789 607 | 2015-08-20 04:15:24.889,32.512411,35.017285,94.3000030517578 608 | 2015-08-20 04:15:25.969,32.512421,35.01727,95.0 609 | 2015-08-20 04:15:31.057,32.51248,35.017192,96.4000015258789 610 | 2015-08-20 04:15:31.836,32.512492,35.017175,96.1999969482422 611 | 2015-08-20 04:15:36.910,32.512551,35.017096,98.1999969482422 612 | 2015-08-20 04:15:37.812,32.512563,35.017081,99.0 613 | 2015-08-20 04:15:42.857,32.512622,35.017014,97.1999969482422 614 | 2015-08-20 04:15:43.904,32.512629,35.016998,96.9000015258789 615 | 2015-08-20 04:15:48.929,32.512679,35.016924,98.0 616 | 2015-08-20 04:15:49.830,32.512686,35.016908,97.8000030517578 617 | 2015-08-20 04:15:54.946,32.512727,35.016819,98.6999969482422 618 | 2015-08-20 04:15:55.863,32.512735,35.0168,97.9000015258789 619 | 2015-08-20 04:15:59.939,32.512763,35.016714,97.0 620 | 2015-08-20 04:16:00.878,32.512769,35.016691,98.0 621 | 2015-08-20 04:16:05.942,32.512842,35.016637,99.8000030517578 622 | 2015-08-20 04:16:06.875,32.512859,35.016648,100.3000030517578 623 | 2015-08-20 04:16:10.956,32.512922,35.016715,100.0 624 | 2015-08-20 04:16:11.879,32.512936,35.016735,100.5 625 | 2015-08-20 04:16:15.900,32.512992,35.016803,101.5 626 | 2015-08-20 04:16:16.890,32.513005,35.016819,100.6999969482422 627 | 2015-08-20 04:16:20.894,32.51307,35.016871,102.4000015258789 628 | 2015-08-20 04:16:21.815,32.513084,35.016884,102.5 629 | 2015-08-20 04:16:27.117,32.513151,35.016949,105.4000015258789 630 | 2015-08-20 04:16:27.981,32.513166,35.016961,105.3000030517578 631 | 2015-08-20 04:16:31.960,32.513221,35.01702,106.5999984741211 632 | 2015-08-20 04:16:32.888,32.513232,35.017037,107.3000030517578 633 | 2015-08-20 04:16:36.966,32.513276,35.017107,106.1999969482422 634 | 2015-08-20 04:16:37.866,32.513287,35.017123,107.4000015258789 635 | 2015-08-20 04:16:43.057,32.51333,35.017208,108.8000030517578 636 | 2015-08-20 04:16:43.873,32.51334,35.017226,110.3000030517578 637 | 2015-08-20 04:16:47.848,32.513398,35.017278,109.5 638 | 2015-08-20 04:16:48.815,32.513411,35.017291,109.9000015258789 639 | 2015-08-20 04:16:52.875,32.513464,35.017348,111.1999969482422 640 | 2015-08-20 04:16:53.817,32.513477,35.017364,112.5 641 | 2015-08-20 04:16:57.999,32.513534,35.017427,113.4000015258789 642 | 2015-08-20 04:16:58.839,32.513548,35.017441,113.6999969482422 643 | 2015-08-20 04:17:03.026,32.513618,35.017485,113.0 644 | 2015-08-20 04:17:03.872,32.513639,35.01749,112.8000030517578 645 | 2015-08-20 04:17:07.843,32.513718,35.017455,113.5999984741211 646 | 2015-08-20 04:17:08.815,32.513731,35.017436,113.6999969482422 647 | 2015-08-20 04:17:11.930,32.51377,35.017373,114.0999984741211 648 | 2015-08-20 04:17:12.887,32.513784,35.017349,114.0 649 | 2015-08-20 04:17:15.874,32.513828,35.017284,114.5 650 | 2015-08-20 04:17:16.827,32.513843,35.017263,114.5999984741211 651 | 2015-08-20 04:17:19.920,32.513887,35.017201,114.4000015258789 652 | 2015-08-20 04:17:20.855,32.513897,35.017175,113.9000015258789 653 | 2015-08-20 04:17:24.052,32.51393,35.017102,113.5 654 | 2015-08-20 04:17:25.054,32.513935,35.017077,113.5999984741211 655 | 2015-08-20 04:17:27.916,32.51393,35.016994,113.9000015258789 656 | 2015-08-20 04:17:28.819,32.513929,35.016965,114.9000015258789 657 | 2015-08-20 04:17:31.911,32.513936,35.016881,114.9000015258789 658 | 2015-08-20 04:17:32.827,32.513941,35.016855,114.3000030517578 659 | 2015-08-20 04:17:36.970,32.513958,35.016764,113.5999984741211 660 | 2015-08-20 04:17:37.876,32.513959,35.016742,113.5999984741211 661 | 2015-08-20 04:17:41.925,32.513989,35.01666,113.5999984741211 662 | 2015-08-20 04:17:42.821,32.513999,35.016639,113.6999969482422 663 | 2015-08-20 04:17:46.931,32.514054,35.016579,111.9000015258789 664 | 2015-08-20 04:17:47.947,32.514065,35.016562,112.8000030517578 665 | 2015-08-20 04:17:51.892,32.514112,35.016492,113.6999969482422 666 | 2015-08-20 04:17:52.841,32.514126,35.016471,113.8000030517578 667 | 2015-08-20 04:17:56.908,32.51417,35.016384,114.1999969482422 668 | 2015-08-20 04:17:57.824,32.514178,35.016362,114.3000030517578 669 | 2015-08-20 04:18:01.921,32.51421,35.016274,114.3000030517578 670 | 2015-08-20 04:18:02.823,32.514221,35.016254,114.1999969482422 671 | 2015-08-20 04:18:06.896,32.514278,35.016184,114.8000030517578 672 | 2015-08-20 04:18:07.813,32.514296,35.016171,115.5999984741211 673 | 2015-08-20 04:18:10.895,32.514355,35.016146,115.5999984741211 674 | 2015-08-20 04:18:13.814,32.514421,35.016143,116.3000030517578 675 | 2015-08-20 04:18:17.869,32.514502,35.016162,117.5999984741211 676 | 2015-08-20 04:18:18.813,32.514522,35.016165,117.5 677 | 2015-08-20 04:18:22.862,32.514598,35.016182,117.4000015258789 678 | 2015-08-20 04:18:23.814,32.514616,35.016187,117.0999984741211 679 | 2015-08-20 04:18:27.863,32.514686,35.016171,116.6999969482422 680 | 2015-08-20 04:18:28.816,32.514703,35.016158,116.5999984741211 681 | 2015-08-20 04:18:32.870,32.514754,35.016085,116.5999984741211 682 | 2015-08-20 04:18:33.815,32.514765,35.01607,115.8000030517578 683 | 2015-08-20 04:18:37.847,32.514825,35.016018,117.3000030517578 684 | 2015-08-20 04:18:38.816,32.514843,35.016007,117.6999969482422 685 | 2015-08-20 04:18:42.882,32.514921,35.015984,117.5999984741211 686 | 2015-08-20 04:18:43.815,32.514942,35.015984,118.3000030517578 687 | 2015-08-20 04:18:47.897,32.51503,35.01597,118.3000030517578 688 | 2015-08-20 04:18:48.836,32.515053,35.01597,118.0999984741211 689 | 2015-08-20 04:18:50.877,32.515114,35.015958,116.4000015258789 690 | 2015-08-20 04:18:51.819,32.515146,35.015951,117.0999984741211 691 | 2015-08-20 04:18:54.007,32.515209,35.015942,117.0999984741211 692 | 2015-08-20 04:18:54.893,32.51524,35.015937,117.5 693 | 2015-08-20 04:18:57.939,32.515328,35.015943,118.5999984741211 694 | 2015-08-20 04:18:58.845,32.515358,35.015946,118.5 695 | 2015-08-20 04:19:01.992,32.515443,35.015964,118.3000030517578 696 | 2015-08-20 04:19:02.889,32.515462,35.015973,118.3000030517578 697 | 2015-08-20 04:19:06.990,32.515544,35.01601,118.6999969482422 698 | 2015-08-20 04:19:07.888,32.51556,35.016017,118.5999984741211 699 | 2015-08-20 04:19:11.886,32.515638,35.016042,118.5999984741211 700 | 2015-08-20 04:19:12.819,32.51566,35.016051,118.0 701 | 2015-08-20 04:19:16.911,32.515738,35.016069,117.9000015258789 702 | 2015-08-20 04:19:17.842,32.515757,35.016076,117.1999969482422 703 | 2015-08-20 04:19:21.908,32.515844,35.016087,116.4000015258789 704 | 2015-08-20 04:19:22.820,32.51587,35.016071,116.1999969482422 705 | 2015-08-20 04:19:25.934,32.515933,35.016016,116.0 706 | 2015-08-20 04:19:26.831,32.515953,35.015993,116.8000030517578 707 | 2015-08-20 04:19:29.879,32.516001,35.015918,116.5 708 | 2015-08-20 04:19:30.833,32.51602,35.015894,117.0 709 | 2015-08-20 04:19:34.152,32.516074,35.015828,116.5 710 | 2015-08-20 04:19:34.904,32.516091,35.015804,116.5 711 | 2015-08-20 04:19:37.851,32.516142,35.01573,116.5999984741211 712 | 2015-08-20 04:19:38.820,32.516157,35.015705,116.5 713 | 2015-08-20 04:19:41.881,32.516211,35.01564,115.4000015258789 714 | 2015-08-20 04:19:42.825,32.516225,35.015617,114.0999984741211 715 | 2015-08-20 04:19:45.963,32.516278,35.015551,113.5 716 | 2015-08-20 04:19:46.890,32.516301,35.015533,112.5 717 | 2015-08-20 04:19:49.987,32.516361,35.015468,112.0 718 | 2015-08-20 04:19:50.878,32.51638,35.015444,112.6999969482422 719 | 2015-08-20 04:19:53.937,32.516442,35.015369,112.5 720 | 2015-08-20 04:19:54.896,32.516462,35.015343,112.5999984741211 721 | 2015-08-20 04:19:58.048,32.516517,35.015264,112.3000030517578 722 | 2015-08-20 04:19:58.898,32.516537,35.015238,112.0999984741211 723 | 2015-08-20 04:20:01.965,32.516592,35.015157,110.0999984741211 724 | 2015-08-20 04:20:02.886,32.51661,35.015129,109.8000030517578 725 | 2015-08-20 04:20:05.959,32.516665,35.015048,107.6999969482422 726 | 2015-08-20 04:20:06.823,32.516683,35.015023,107.5 727 | 2015-08-20 04:20:09.926,32.516736,35.014943,107.5999984741211 728 | 2015-08-20 04:20:10.824,32.516754,35.014916,107.0999984741211 729 | 2015-08-20 04:20:14.040,32.5168,35.014835,106.0999984741211 730 | 2015-08-20 04:20:14.888,32.516818,35.01481,105.1999969482422 731 | 2015-08-20 04:20:17.897,32.516873,35.014737,106.4000015258789 732 | 2015-08-20 04:20:18.844,32.516889,35.014709,106.4000015258789 733 | 2015-08-20 04:20:21.996,32.516933,35.014617,103.5 734 | 2015-08-20 04:20:22.897,32.516945,35.014584,103.0999984741211 735 | 2015-08-20 04:20:24.905,32.516971,35.014518,103.8000030517578 736 | 2015-08-20 04:20:25.835,32.516984,35.014484,104.1999969482422 737 | 2015-08-20 04:20:28.982,32.51702,35.014387,104.8000030517578 738 | 2015-08-20 04:20:29.923,32.517035,35.014355,105.1999969482422 739 | 2015-08-20 04:20:32.863,32.517087,35.014279,102.9000015258789 740 | 2015-08-20 04:20:33.994,32.517098,35.014264,102.4000015258789 741 | 2015-08-20 04:20:42.329,32.517142,35.014212,103.4000015258789 742 | -------------------------------------------------------------------------------- /Ch03/03_05/speed.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | csv_file = 'track.csv' 5 | df = pd.read_csv(csv_file, parse_dates=['time']) 6 | df 7 | 8 | # %% 9 | import numpy as np 10 | 11 | lat_km = 92 12 | lng_km = 111 13 | 14 | 15 | def distance(lat1, lng1, lat2, lng2): 16 | delta_lat = (lat1 - lat2) * lat_km 17 | delta_lng = (lng1 - lng2) * lng_km 18 | return np.hypot(delta_lat, delta_lng) 19 | # %% 20 | lat1, lng1 = df.loc[200]['lat'], df.iloc[200]['lng'] 21 | lat2, lng2 = df.loc[201]['lat'], df.iloc[201]['lng'] 22 | distance(lat1, lng1, lat2, lng2) 23 | # %% 24 | s = pd.Series(np.arange(5)) 25 | s 26 | 27 | # %% 28 | s.shift() 29 | 30 | # %% 31 | s.shift(-1) 32 | 33 | 34 | # %% 35 | dist = distance( 36 | df['lat'], df['lng'], 37 | df['lat'].shift(), df['lng'].shift(), 38 | ) 39 | dist[:5] 40 | 41 | # %% 42 | dist.sum() 43 | 44 | # %% 45 | times = df['time'].diff() 46 | times[:5] 47 | 48 | # %% 49 | times.sum() 50 | 51 | # %% 52 | 53 | times_hour = times / pd.Timedelta(1, 'hour') 54 | times_hour[:5] 55 | 56 | # %% 57 | speed = dist / times_hour 58 | speed[:5] -------------------------------------------------------------------------------- /Ch03/03_06/box_plot.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | import numpy as np 4 | 5 | csv_file = 'track.csv' 6 | df = pd.read_csv(csv_file, parse_dates=['time']) 7 | 8 | lat_km = 92 9 | lng_km = 111 10 | 11 | def distance(lat1, lng1, lat2, lng2): 12 | delta_lat = (lat1 - lat2) * lat_km 13 | delta_lng = (lng1 - lng2) * lng_km 14 | return np.hypot(delta_lat, delta_lng) 15 | 16 | dist = distance( 17 | df['lat'], df['lng'], 18 | df['lat'].shift(), df['lng'].shift(), 19 | ) 20 | times = df['time'].diff() 21 | times_hour = times / pd.Timedelta(1, 'hour') 22 | speed = dist / times_hour 23 | 24 | # %% 25 | speed.describe() 26 | 27 | # %% 28 | speed.plot.box() 29 | 30 | # %% 31 | import matplotlib.pyplot as plt 32 | 33 | plt.rcParams['figure.figsize'] = (10, 6) 34 | plt.style.use('seaborn-whitegrid') 35 | speed.plot.box() 36 | 37 | # %% 38 | speed.name = '' 39 | ax = speed.plot.box(title="Miki's Run") 40 | ax.set_ylabel( 41 | r'Running speed $\frac{km}{h}$') 42 | # %% 43 | -------------------------------------------------------------------------------- /Ch03/challenge/download_data.py: -------------------------------------------------------------------------------- 1 | from urllib.request import urlretrieve 2 | 3 | dl_size = 0 4 | 5 | def reporthook(_, size, total): 6 | global dl_size 7 | 8 | dl_size += size 9 | precent = dl_size / total * 100 10 | print(f' {precent:.2f}%', end='\r') 11 | 12 | data_url = 'https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2020-08.parquet' 13 | out_file = 'taxi.parquet' 14 | urlretrieve(data_url, out_file, reporthook=reporthook) 15 | -------------------------------------------------------------------------------- /Ch03/challenge/mean_speed.py: -------------------------------------------------------------------------------- 1 | # What is the mean speed (mile/hour) in taxi.parquet? 2 | 3 | # - Run download_data.py to download the data 4 | -------------------------------------------------------------------------------- /Ch03/solution/download_data.py: -------------------------------------------------------------------------------- 1 | from urllib.request import urlretrieve 2 | 3 | dl_size = 0 4 | 5 | def reporthook(_, size, total): 6 | global dl_size 7 | 8 | dl_size += size 9 | precent = dl_size / total * 100 10 | print(f' {precent:.2f}%', end='\r') 11 | 12 | data_url = 'https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2020-08.parquet' 13 | out_file = 'taxi.parquet' 14 | urlretrieve(data_url, out_file, reporthook=reporthook) 15 | -------------------------------------------------------------------------------- /Ch03/solution/mean_speed.py: -------------------------------------------------------------------------------- 1 | # What is the mean speed (mile/hour) in taxi.parquet? 2 | 3 | # - Run download_data.py to download the data 4 | 5 | # %% 6 | import pandas as pd 7 | 8 | df = pd.read_parquet('taxi.parquet') 9 | 10 | # %% 11 | mask = df['tpep_dropoff_datetime'] > df['tpep_pickup_datetime'] 12 | df = df[mask] 13 | 14 | # %% 15 | times = df['tpep_dropoff_datetime'] - df['tpep_pickup_datetime'] 16 | times_hour = times / pd.Timedelta(1, 'hour') 17 | speed = df['trip_distance'] / times_hour 18 | 19 | # %% 20 | speed.mean() 21 | -------------------------------------------------------------------------------- /Ch04/04_01/map.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | df = pd.read_csv( 5 | 'track.csv', 6 | parse_dates=['time'], 7 | index_col='time', 8 | ) 9 | df.columns 10 | 11 | # %% 12 | df.index[:5] 13 | 14 | # %% 15 | import folium 16 | 17 | center = [df['lat'].mean(), df['lng'].mean()] 18 | m = folium.Map( 19 | location=center, 20 | zoom_start=15, 21 | ) 22 | m 23 | 24 | # %% 25 | m.save('track.html') -------------------------------------------------------------------------------- /Ch04/04_01/track.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 25 | 34 | 35 | 36 | 37 | 38 |
39 | 40 | 41 | -------------------------------------------------------------------------------- /Ch04/04_02/track.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | df = pd.read_csv( 5 | 'track.csv', 6 | parse_dates=['time'], 7 | index_col='time', 8 | ) 9 | 10 | # %% 11 | import folium 12 | 13 | center = [df['lat'].mean(), df['lng'].mean()] 14 | m = folium.Map( 15 | location=center, 16 | zoom_start=15, 17 | ) 18 | 19 | loc = tuple(df.iloc[100][['lat', 'lng']]) 20 | marker = folium.Marker(loc) 21 | marker.add_to(m) 22 | m 23 | 24 | # %% 25 | m = folium.Map( 26 | location=center, 27 | zoom_start=15, 28 | ) 29 | marker = folium.CircleMarker( 30 | loc, 31 | color='red') 32 | marker.add_to(m) 33 | m 34 | 35 | # %% 36 | m = folium.Map( 37 | location=center, 38 | zoom_start=15, 39 | ) 40 | marker = folium.CircleMarker( 41 | loc, 42 | color='red', 43 | popup='Hi there', 44 | ) 45 | marker.add_to(m) 46 | m 47 | 48 | # %% 49 | m = folium.Map( 50 | location=center, 51 | zoom_start=15, 52 | ) 53 | 54 | def add_marker(row): 55 | loc = tuple(row[['lat', 'lng']]) 56 | marker = folium.CircleMarker( 57 | loc, 58 | radius=5, 59 | color='red', 60 | popup=row.name.strftime('%H:%M'), 61 | ) 62 | marker.add_to(m) 63 | 64 | add_marker(df.iloc[200]) 65 | m 66 | # %% 67 | m = folium.Map( 68 | location=center, 69 | zoom_start=15, 70 | ) 71 | df.apply(add_marker, axis=1) 72 | m 73 | 74 | # %% 75 | m = folium.Map( 76 | location=center, 77 | zoom_start=15, 78 | ) 79 | min_df = df.resample('min').mean() 80 | min_df.apply(add_marker, axis=1) 81 | m -------------------------------------------------------------------------------- /Ch04/04_03/geo.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from shapely.geometry import Point 3 | 4 | pt = Point(1, 2) 5 | pt 6 | 7 | # %% 8 | pt.x, pt.y 9 | 10 | # %% 11 | from shapely.geometry import Polygon 12 | 13 | poly = Polygon([ 14 | [0, 0], 15 | [0, 10], 16 | [10, 10], 17 | [10, 0], 18 | ]) 19 | poly 20 | 21 | # %% 22 | poly.area 23 | 24 | # %% 25 | poly.centroid 26 | 27 | # %% 28 | poly.intersects(pt) 29 | 30 | # %% 31 | poly.intersects(Point(10, 20)) 32 | 33 | # %% 34 | import pandas as pd 35 | 36 | df = pd.read_csv( 37 | 'track.csv', 38 | parse_dates=['time'], 39 | index_col='time', 40 | ) 41 | df = df.resample('min').mean() 42 | 43 | # %% 44 | df['point'] = ( 45 | df[['lat', 'lng']] 46 | .apply(Point, axis=1) 47 | ) 48 | df.head() 49 | 50 | # %% 51 | mid_lat, max_lat = \ 52 | df['lat'].mean(), df['lat'].max() 53 | mid_lng, max_lng = \ 54 | df['lng'].mean(), df['lng'].max() 55 | poly = Polygon([ 56 | [mid_lat, mid_lng], 57 | [mid_lat, max_lng], 58 | [max_lat, max_lng], 59 | [max_lat, mid_lng], 60 | ]) 61 | poly 62 | 63 | # %% 64 | df[df['point'].apply(poly.intersects)] 65 | 66 | # %% 67 | poly.exterior.xy 68 | # %% 69 | import numpy as np 70 | 71 | np.stack(poly.exterior.xy) 72 | 73 | # %% 74 | np.stack(poly.exterior.xy).T 75 | 76 | # %% 77 | import folium 78 | 79 | m = folium.Map( 80 | location=[df['lat'].mean(), df['lng'].mean()], 81 | zoom_start=15, 82 | ) 83 | points = np.stack(poly.exterior.xy).T 84 | m.add_child(folium.PolyLine(points)) 85 | m 86 | 87 | # %% 88 | center = [df['lat'].mean(), df['lng'].mean()] 89 | m = folium.Map( 90 | location=center, 91 | zoom_start=15, 92 | ) 93 | m.add_child(folium.PolyLine(points)) 94 | 95 | def add_marker(row): 96 | loc = tuple(row[['lat', 'lng']]) 97 | in_poly = poly.intersects(row['point']) 98 | marker = folium.CircleMarker( 99 | loc, 100 | radius=5, 101 | color='yellow' if in_poly else 'green', 102 | popup=row.name.strftime('%H:%M'), 103 | ) 104 | marker.add_to(m) 105 | 106 | df.apply(add_marker, axis=1) 107 | m -------------------------------------------------------------------------------- /Ch04/challenge/height.py: -------------------------------------------------------------------------------- 1 | # Draw the running track from track.csv 2 | # 3 | # - Sample data to a minute interval 4 | # - Markers should be blue if the height is below 100 meter, otherwise red -------------------------------------------------------------------------------- /Ch04/solution/height.py: -------------------------------------------------------------------------------- 1 | # Draw the running track from track.csv 2 | # 3 | # - Sample data to a minute interval 4 | # - Markers should be blue if the height is below 100 meter, otherwise red 5 | 6 | # %% 7 | import pandas as pd 8 | 9 | df = pd.read_csv( 10 | 'track.csv', 11 | parse_dates=['time'], 12 | index_col='time', 13 | ) 14 | df = df.resample('min').mean() 15 | # %% 16 | import folium 17 | 18 | m = folium.Map( 19 | location=[df['lat'].mean(), df['lng'].mean()], 20 | zoom_start=15, 21 | ) 22 | 23 | def add_marker(row): 24 | loc = tuple(row[['lat', 'lng']]) 25 | marker = folium.CircleMarker( 26 | loc, 27 | radius=5, 28 | color='blue' if row['height'] < 100 else 'red', 29 | popup=row.name.strftime('%H:%M'), 30 | ) 31 | marker.add_to(m) 32 | 33 | df.apply(add_marker, axis=1) 34 | m -------------------------------------------------------------------------------- /Ch05/05_01/download_data.py: -------------------------------------------------------------------------------- 1 | """Download 10% of January 2016 taxi data to taxi.csv""" 2 | 3 | from urllib.request import urlopen 4 | from time import monotonic 5 | from pathlib import Path 6 | 7 | here = Path(__file__).absolute().parent 8 | csv_file = here / 'taxi.csv' 9 | url = 'https://s3.amazonaws.com/nyc-tlc/csv_backup/yellow_tripdata_2016-01.csv' 10 | out_name = '/'.join(csv_file.parts[-3:]) 11 | 12 | start = monotonic() 13 | with urlopen(url) as resp: 14 | payload_size = int(resp.headers['Content-Length']) 15 | size_mb = payload_size / (2**20) 16 | print(f'downloading 10% of {size_mb:.2f}MB to {out_name}') 17 | size = 0 18 | with open(csv_file, 'wb') as out: 19 | for i, line in enumerate(resp): 20 | size += len(line) 21 | prec = (size / payload_size) * 100 22 | print(f' {prec:.2f}%', end='\r') 23 | if i % 10 != 0: 24 | continue 25 | out.write(line) 26 | 27 | if size != payload_size: 28 | raise SystemError(f'error: expected {payload_size} bytes, got {size}') 29 | 30 | duration = int(monotonic() - start) 31 | print(f'{csv_file}: {size_mb:.1f}MB downloaded in {duration} seconds') 32 | -------------------------------------------------------------------------------- /Ch05/05_01/first_look.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from pathlib import Path 3 | 4 | csv_file = Path('taxi.csv') 5 | mb = 2**20 6 | csv_file.stat().st_size / mb 7 | 8 | # %% 9 | num_lines = 0 10 | with csv_file.open() as fp: 11 | for lnum, line in enumerate(fp): 12 | if lnum < 5: 13 | print(line, end='') 14 | num_lines += 1 15 | 16 | print(f'lines: {num_lines:,}') -------------------------------------------------------------------------------- /Ch05/05_02/download_data.py: -------------------------------------------------------------------------------- 1 | """Download 10% of January 2016 taxi data to taxi.csv""" 2 | 3 | from urllib.request import urlopen 4 | from time import monotonic 5 | from pathlib import Path 6 | 7 | here = Path(__file__).absolute().parent 8 | csv_file = here / 'taxi.csv' 9 | url = 'https://s3.amazonaws.com/nyc-tlc/csv_backup/yellow_tripdata_2016-01.csv' 10 | out_name = '/'.join(csv_file.parts[-3:]) 11 | 12 | start = monotonic() 13 | with urlopen(url) as resp: 14 | payload_size = int(resp.headers['Content-Length']) 15 | size_mb = payload_size / (2**20) 16 | print(f'downloading 10% of {size_mb:.2f}MB to {out_name}') 17 | size = 0 18 | with open(csv_file, 'wb') as out: 19 | for i, line in enumerate(resp): 20 | size += len(line) 21 | prec = (size / payload_size) * 100 22 | print(f' {prec:.2f}%', end='\r') 23 | if i % 10 != 0: 24 | continue 25 | out.write(line) 26 | 27 | if size != payload_size: 28 | raise SystemError(f'error: expected {payload_size} bytes, got {size}') 29 | 30 | duration = int(monotonic() - start) 31 | print(f'{csv_file}: {size_mb:.1f}MB downloaded in {duration} seconds') 32 | -------------------------------------------------------------------------------- /Ch05/05_02/load.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | csv_file = 'taxi.csv' 5 | df = pd.read_csv(csv_file) 6 | print(f'{len(df):,}') 7 | 8 | # %% 9 | df.iloc[0] 10 | 11 | # %% 12 | df.dtypes 13 | 14 | # %% 15 | time_cols = [ 16 | 'tpep_pickup_datetime', 17 | 'tpep_dropoff_datetime', 18 | ] 19 | df = pd.read_csv( 20 | csv_file, 21 | parse_dates=time_cols, 22 | ) 23 | df.dtypes -------------------------------------------------------------------------------- /Ch05/05_03/categorical.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | time_cols = [ 5 | 'tpep_pickup_datetime', 6 | 'tpep_dropoff_datetime', 7 | ] 8 | df = pd.read_csv( 9 | 'taxi.csv', 10 | parse_dates=time_cols, 11 | ) 12 | df.dtypes 13 | 14 | # %% 15 | df['VendorID'].unique() 16 | 17 | # %% 18 | vendors = { 19 | 1: 'Creative', 20 | 2: 'VeriFone', 21 | } 22 | df['Vendor'] = df['VendorID'].map(vendors) 23 | df['Vendor'].head() 24 | 25 | # %% 26 | mb = 2**20 27 | df['Vendor'].memory_usage(deep=True) / mb 28 | 29 | # %% 30 | df['Vendor'] = ( 31 | df['VendorID'] 32 | .map(vendors) 33 | .astype('category') 34 | ) 35 | df['Vendor'].head() 36 | 37 | # %% 38 | df['Vendor'].memory_usage(deep=True) / mb 39 | 40 | # %% 41 | df['Vendor'].head().cat.codes 42 | 43 | # %% 44 | len(df[df['Vendor'] == 'VeriFone']) -------------------------------------------------------------------------------- /Ch05/05_03/download_data.py: -------------------------------------------------------------------------------- 1 | """Download 10% of January 2016 taxi data to taxi.csv""" 2 | 3 | from urllib.request import urlopen 4 | from time import monotonic 5 | from pathlib import Path 6 | 7 | here = Path(__file__).absolute().parent 8 | csv_file = here / 'taxi.csv' 9 | url = 'https://s3.amazonaws.com/nyc-tlc/csv_backup/yellow_tripdata_2016-01.csv' 10 | out_name = '/'.join(csv_file.parts[-3:]) 11 | 12 | start = monotonic() 13 | with urlopen(url) as resp: 14 | payload_size = int(resp.headers['Content-Length']) 15 | size_mb = payload_size / (2**20) 16 | print(f'downloading 10% of {size_mb:.2f}MB to {out_name}') 17 | size = 0 18 | with open(csv_file, 'wb') as out: 19 | for i, line in enumerate(resp): 20 | size += len(line) 21 | prec = (size / payload_size) * 100 22 | print(f' {prec:.2f}%', end='\r') 23 | if i % 10 != 0: 24 | continue 25 | out.write(line) 26 | 27 | if size != payload_size: 28 | raise SystemError(f'error: expected {payload_size} bytes, got {size}') 29 | 30 | duration = int(monotonic() - start) 31 | print(f'{csv_file}: {size_mb:.1f}MB downloaded in {duration} seconds') 32 | -------------------------------------------------------------------------------- /Ch05/05_04/daily.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | time_cols = [ 5 | 'tpep_pickup_datetime', 6 | 'tpep_dropoff_datetime', 7 | ] 8 | df = pd.read_csv( 9 | 'taxi.csv', 10 | parse_dates=time_cols, 11 | ) 12 | vendors = { 13 | 1: 'Creative', 14 | 2: 'VeriFone', 15 | } 16 | df['Vendor'] = \ 17 | df['VendorID'].map(vendors).astype('category') 18 | 19 | # %% 20 | df['tpep_pickup_datetime'].head() 21 | 22 | # %% 23 | ts = pd.Timestamp(2021, 11, 1, 14, 37, 39, 3920) 24 | ts 25 | 26 | # %% 27 | ts.floor('D') 28 | 29 | # %% 30 | df['tpep_pickup_datetime'].head().floor('D') 31 | 32 | # %% 33 | df['tpep_pickup_datetime'].head().dt.floor('D') 34 | 35 | # %% 36 | keys = \ 37 | df['tpep_pickup_datetime'].dt.floor('D') 38 | df.groupby(keys) 39 | 40 | # %% 41 | df.groupby(keys).count() 42 | 43 | # %% 44 | ( 45 | df.groupby(keys) 46 | .count() 47 | ['VendorID'] 48 | .plot.bar() 49 | ) 50 | 51 | # %% 52 | s = ( 53 | df.groupby(keys) 54 | .count() 55 | ['VendorID'] 56 | ) 57 | s.index = s.index.day 58 | s.index.name = 'Date' 59 | ax = s.plot.bar( 60 | title='Daily Taxi Rides (Jan 2016)', 61 | rot=0, 62 | ) 63 | ax.set_ylabel('Number of Rides') 64 | -------------------------------------------------------------------------------- /Ch05/05_04/download_data.py: -------------------------------------------------------------------------------- 1 | """Download 10% of January 2016 taxi data to taxi.csv""" 2 | 3 | from urllib.request import urlopen 4 | from time import monotonic 5 | from pathlib import Path 6 | 7 | here = Path(__file__).absolute().parent 8 | csv_file = here / 'taxi.csv' 9 | url = 'https://s3.amazonaws.com/nyc-tlc/csv_backup/yellow_tripdata_2016-01.csv' 10 | out_name = '/'.join(csv_file.parts[-3:]) 11 | 12 | start = monotonic() 13 | with urlopen(url) as resp: 14 | payload_size = int(resp.headers['Content-Length']) 15 | size_mb = payload_size / (2**20) 16 | print(f'downloading 10% of {size_mb:.2f}MB to {out_name}') 17 | size = 0 18 | with open(csv_file, 'wb') as out: 19 | for i, line in enumerate(resp): 20 | size += len(line) 21 | prec = (size / payload_size) * 100 22 | print(f' {prec:.2f}%', end='\r') 23 | if i % 10 != 0: 24 | continue 25 | out.write(line) 26 | 27 | if size != payload_size: 28 | raise SystemError(f'error: expected {payload_size} bytes, got {size}') 29 | 30 | duration = int(monotonic() - start) 31 | print(f'{csv_file}: {size_mb:.1f}MB downloaded in {duration} seconds') 32 | -------------------------------------------------------------------------------- /Ch05/05_05/download_data.py: -------------------------------------------------------------------------------- 1 | """Download 10% of January 2016 taxi data to taxi.csv""" 2 | 3 | from urllib.request import urlopen 4 | from time import monotonic 5 | from pathlib import Path 6 | 7 | here = Path(__file__).absolute().parent 8 | csv_file = here / 'taxi.csv' 9 | url = 'https://s3.amazonaws.com/nyc-tlc/csv_backup/yellow_tripdata_2016-01.csv' 10 | out_name = '/'.join(csv_file.parts[-3:]) 11 | 12 | start = monotonic() 13 | with urlopen(url) as resp: 14 | payload_size = int(resp.headers['Content-Length']) 15 | size_mb = payload_size / (2**20) 16 | print(f'downloading 10% of {size_mb:.2f}MB to {out_name}') 17 | size = 0 18 | with open(csv_file, 'wb') as out: 19 | for i, line in enumerate(resp): 20 | size += len(line) 21 | prec = (size / payload_size) * 100 22 | print(f' {prec:.2f}%', end='\r') 23 | if i % 10 != 0: 24 | continue 25 | out.write(line) 26 | 27 | if size != payload_size: 28 | raise SystemError(f'error: expected {payload_size} bytes, got {size}') 29 | 30 | duration = int(monotonic() - start) 31 | print(f'{csv_file}: {size_mb:.1f}MB downloaded in {duration} seconds') 32 | -------------------------------------------------------------------------------- /Ch05/05_05/hourly.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | time_cols = [ 5 | 'tpep_pickup_datetime', 6 | 'tpep_dropoff_datetime', 7 | ] 8 | df = pd.read_csv( 9 | 'taxi.csv', 10 | parse_dates=time_cols, 11 | ) 12 | vendors = { 13 | 1: 'Creative', 14 | 2: 'VeriFone', 15 | } 16 | df['Vendor'] = \ 17 | df['VendorID'].map(vendors).astype('category') 18 | 19 | # %% 20 | df['hour'] = \ 21 | df['tpep_pickup_datetime'].dt.hour 22 | df['day'] = \ 23 | df['tpep_pickup_datetime'].dt.date 24 | df[['day', 'hour']].head() 25 | 26 | # %% 27 | ( 28 | df.groupby(['Vendor', 'day', 'hour']) 29 | .count() 30 | ['Vendor'] 31 | ) 32 | 33 | # %% 34 | ( 35 | df.groupby(['Vendor', 'day', 'hour']) 36 | .count() 37 | .index 38 | ) 39 | 40 | # %% 41 | ( 42 | df.groupby( 43 | ['Vendor', 'day', 'hour'], 44 | as_index=False 45 | ) 46 | .count() 47 | .columns 48 | ) 49 | 50 | # %% 51 | daily_df = ( 52 | df 53 | .groupby(['Vendor', 'day', 'hour'], as_index=False) 54 | .count() 55 | ) 56 | hourly_df = ( 57 | daily_df 58 | .groupby(['Vendor', 'hour'], as_index=False) 59 | ['VendorID'] 60 | .median() 61 | ) 62 | hourly_df 63 | 64 | # %% 65 | hourly_df.pivot( 66 | columns='Vendor', 67 | index='hour', 68 | values='VendorID' 69 | ).plot.bar(rot=0) -------------------------------------------------------------------------------- /Ch05/05_06/download_data.py: -------------------------------------------------------------------------------- 1 | """Download 10% of January 2016 taxi data to taxi.csv""" 2 | 3 | from urllib.request import urlopen 4 | from time import monotonic 5 | from pathlib import Path 6 | 7 | here = Path(__file__).absolute().parent 8 | csv_file = here / 'taxi.csv' 9 | url = 'https://s3.amazonaws.com/nyc-tlc/csv_backup/yellow_tripdata_2016-01.csv' 10 | out_name = '/'.join(csv_file.parts[-3:]) 11 | 12 | start = monotonic() 13 | with urlopen(url) as resp: 14 | payload_size = int(resp.headers['Content-Length']) 15 | size_mb = payload_size / (2**20) 16 | print(f'downloading 10% of {size_mb:.2f}MB to {out_name}') 17 | size = 0 18 | with open(csv_file, 'wb') as out: 19 | for i, line in enumerate(resp): 20 | size += len(line) 21 | prec = (size / payload_size) * 100 22 | print(f' {prec:.2f}%', end='\r') 23 | if i % 10 != 0: 24 | continue 25 | out.write(line) 26 | 27 | if size != payload_size: 28 | raise SystemError(f'error: expected {payload_size} bytes, got {size}') 29 | 30 | duration = int(monotonic() - start) 31 | print(f'{csv_file}: {size_mb:.1f}MB downloaded in {duration} seconds') 32 | -------------------------------------------------------------------------------- /Ch05/05_06/download_weather.py: -------------------------------------------------------------------------------- 1 | from urllib.request import urlretrieve 2 | from pathlib import Path 3 | 4 | here = Path(__file__).absolute().parent 5 | csv_file = here / 'weather.csv' 6 | 7 | url = ( 8 | 'https://www.ncei.noaa.gov/data/' 9 | 'global-historical-climatology-network-daily/access/USW00094789.csv' 10 | ) 11 | 12 | out_name = '/'.join(csv_file.parts[-3:]) 13 | print(f'downloading weather to {out_name}') 14 | urlretrieve(url, csv_file) -------------------------------------------------------------------------------- /Ch05/05_06/weather.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | time_cols = [ 5 | 'tpep_pickup_datetime', 6 | 'tpep_dropoff_datetime', 7 | ] 8 | df = pd.read_csv( 9 | 'taxi.csv', 10 | parse_dates=time_cols, 11 | ) 12 | vendors = { 13 | 1: 'Creative', 14 | 2: 'VeriFone', 15 | } 16 | df['Vendor'] = \ 17 | df['VendorID'].map(vendors).astype('category') 18 | 19 | # %% 20 | wdf = pd.read_csv( 21 | 'weather.csv', 22 | parse_dates=['DATE'], 23 | index_col='DATE' 24 | ) 25 | wdf.head() 26 | 27 | # %% 28 | wdf.describe() 29 | 30 | # %% 31 | from scipy.constants import convert_temperature 32 | 33 | wdf['tempF'] = convert_temperature( 34 | wdf['TMAX']/10, 'C', 'F') 35 | wdf['tempF'].sample(10) 36 | 37 | # %% 38 | ddf = ( 39 | df.groupby( 40 | df['tpep_pickup_datetime'].dt.date) 41 | .count() 42 | ) 43 | ddf.head() 44 | 45 | # %% 46 | jdf = pd.merge( 47 | ddf, wdf, 48 | left_index=True, right_index=True, 49 | ) 50 | jdf.head() 51 | 52 | # %% 53 | jdf.plot.scatter(x='tempF', y='Vendor'); -------------------------------------------------------------------------------- /Ch05/challenge/by_count.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LinkedInLearning/data-science-foundations-python-scientific-stack-3084641/cb2d7702e66ff3e80816d9f49844c80c18cfa0ba/Ch05/challenge/by_count.png -------------------------------------------------------------------------------- /Ch05/challenge/by_weekday.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LinkedInLearning/data-science-foundations-python-scientific-stack-3084641/cb2d7702e66ff3e80816d9f49844c80c18cfa0ba/Ch05/challenge/by_weekday.png -------------------------------------------------------------------------------- /Ch05/challenge/download_data.py: -------------------------------------------------------------------------------- 1 | """Download 10% of January 2016 taxi data to taxi.csv""" 2 | 3 | from urllib.request import urlopen 4 | from time import monotonic 5 | from pathlib import Path 6 | 7 | here = Path(__file__).absolute().parent 8 | csv_file = here / 'taxi.csv' 9 | url = 'https://s3.amazonaws.com/nyc-tlc/csv_backup/yellow_tripdata_2016-01.csv' 10 | out_name = '/'.join(csv_file.parts[-3:]) 11 | 12 | start = monotonic() 13 | with urlopen(url) as resp: 14 | payload_size = int(resp.headers['Content-Length']) 15 | size_mb = payload_size / (2**20) 16 | print(f'downloading 10% of {size_mb:.2f}MB to {out_name}') 17 | size = 0 18 | with open(csv_file, 'wb') as out: 19 | for i, line in enumerate(resp): 20 | size += len(line) 21 | prec = (size / payload_size) * 100 22 | print(f' {prec:.2f}%', end='\r') 23 | if i % 10 != 0: 24 | continue 25 | out.write(line) 26 | 27 | if size != payload_size: 28 | raise SystemError(f'error: expected {payload_size} bytes, got {size}') 29 | 30 | duration = int(monotonic() - start) 31 | print(f'{csv_file}: {size_mb:.1f}MB downloaded in {duration} seconds') 32 | -------------------------------------------------------------------------------- /Ch05/challenge/tip.py: -------------------------------------------------------------------------------- 1 | """ 2 | - Load the taxi data. 3 | - Remove all rows with either total_amount <= 0 or 4 | passenger_count == 0 5 | - Create a bar chart of average tip % per passenger_count 6 | - Create a bar chart of average tip % per day of week 7 | """ 8 | -------------------------------------------------------------------------------- /Ch05/solution/download_data.py: -------------------------------------------------------------------------------- 1 | """Download 10% of January 2016 taxi data to taxi.csv""" 2 | 3 | from urllib.request import urlopen 4 | from time import monotonic 5 | from pathlib import Path 6 | 7 | here = Path(__file__).absolute().parent 8 | csv_file = here / 'taxi.csv' 9 | url = 'https://s3.amazonaws.com/nyc-tlc/csv_backup/yellow_tripdata_2016-01.csv' 10 | out_name = '/'.join(csv_file.parts[-3:]) 11 | 12 | start = monotonic() 13 | with urlopen(url) as resp: 14 | payload_size = int(resp.headers['Content-Length']) 15 | size_mb = payload_size / (2**20) 16 | print(f'downloading 10% of {size_mb:.2f}MB to {out_name}') 17 | size = 0 18 | with open(csv_file, 'wb') as out: 19 | for i, line in enumerate(resp): 20 | size += len(line) 21 | prec = (size / payload_size) * 100 22 | print(f' {prec:.2f}%', end='\r') 23 | if i % 10 != 0: 24 | continue 25 | out.write(line) 26 | 27 | if size != payload_size: 28 | raise SystemError(f'error: expected {payload_size} bytes, got {size}') 29 | 30 | duration = int(monotonic() - start) 31 | print(f'{csv_file}: {size_mb:.1f}MB downloaded in {duration} seconds') 32 | -------------------------------------------------------------------------------- /Ch05/solution/tip.py: -------------------------------------------------------------------------------- 1 | """ 2 | - Load the taxi data. 3 | - Remove all rows with either total_amount <= 0 or 4 | passenger_count == 0 5 | - Create a bar chart of average tip % per passenger_count 6 | - Create a bar chart of average tip % per day of week 7 | """ 8 | 9 | # %% 10 | import pandas as pd 11 | 12 | time_cols = [ 13 | 'tpep_pickup_datetime', 14 | 'tpep_dropoff_datetime', 15 | ] 16 | df = pd.read_csv( 17 | 'taxi.csv', 18 | parse_dates=time_cols, 19 | ) 20 | vendors = { 21 | 1: 'Creative', 22 | 2: 'VeriFone', 23 | } 24 | df['Vendor'] = \ 25 | df['VendorID'].map(vendors).astype('category') 26 | len(df) 27 | 28 | # %% Drop bad data 29 | bad = df[ 30 | (df['total_amount'] <= 0) | 31 | (df['passenger_count'] == 0) 32 | ] 33 | df.drop(bad.index, inplace=True) 34 | len(df) 35 | 36 | # %% Helper column 37 | df['tip_pct'] = \ 38 | df['tip_amount'] / df['total_amount'] * 100 39 | 40 | # %% Passenger Count 41 | 42 | by_count = ( 43 | df.groupby('passenger_count') 44 | ['tip_pct'] 45 | .mean() 46 | ) 47 | ax = by_count.plot.bar( 48 | title='Tip % by Passenger Count', 49 | rot=0, 50 | ) 51 | ax.set_ylabel('Tip %') 52 | 53 | # %% Week Day 54 | from calendar import day_abbr 55 | 56 | day_of_week = \ 57 | df['tpep_pickup_datetime'].dt.weekday 58 | by_day = ( 59 | df.groupby(day_of_week) 60 | ['tip_pct'] 61 | .mean() 62 | ) 63 | by_day.index.name = 'Day of Week' 64 | ax = by_day.plot.bar(title='Tip % by Day', rot=45) 65 | ax.set_ylabel('Tip %') 66 | ax.set_xticklabels( 67 | [day_abbr[v] for v in ax.get_xticks()]) 68 | None # quell ax output 69 | # %% 70 | -------------------------------------------------------------------------------- /Ch06/06_02/regression.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import fetch_california_housing 3 | 4 | cal_housing = fetch_california_housing() 5 | 6 | # %% 7 | type(cal_housing) 8 | 9 | # %% 10 | cal_housing.keys() 11 | 12 | # %% 13 | type(cal_housing['data']) 14 | 15 | # %% 16 | cal_housing['feature_names'] 17 | 18 | # %% 19 | print(cal_housing['DESCR']) 20 | 21 | # %% 22 | cal_housing['target'][:10] 23 | 24 | # %% 25 | from sklearn.ensemble import RandomForestRegressor 26 | 27 | X, y = cal_housing['data'], cal_housing['target'] 28 | 29 | clf = RandomForestRegressor() 30 | clf.fit(X, y) # train 31 | 32 | # %% 33 | clf.score(X, y) 34 | 35 | # %% 36 | clf.score? 37 | 38 | # %% 39 | dir(clf) 40 | 41 | # %% 42 | clf.n_features_in_ 43 | 44 | # %% 45 | X.shape 46 | 47 | # %% 48 | from sklearn.tree import export_graphviz 49 | 50 | export_graphviz( 51 | clf.estimators_[0], 52 | 'tree.dot', 53 | feature_names=cal_housing['feature_names'], 54 | max_depth=5, 55 | ) 56 | # %% 57 | !dot -Tsvg -o tree.svg tree.dot 58 | 59 | # %% 60 | from IPython import display 61 | 62 | display.SVG('tree.svg') 63 | 64 | # %% 65 | i = 17 66 | row = X[i] 67 | clf.predict(row) 68 | 69 | # %% 70 | row.shape 71 | 72 | #%% 73 | 74 | row = X[i:i+1] 75 | row.shape 76 | 77 | # %% 78 | clf.predict(row) 79 | 80 | # %% 81 | y[i] -------------------------------------------------------------------------------- /Ch06/06_03/split.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import fetch_california_housing 3 | 4 | cal_housing = fetch_california_housing() 5 | X, y = cal_housing['data'], cal_housing['target'] 6 | X.shape 7 | 8 | # %% 9 | from sklearn.model_selection import train_test_split 10 | 11 | X_train, X_test, y_train, y_test = train_test_split( 12 | X, y, 13 | test_size=0.3, 14 | ) 15 | X_train.shape 16 | 17 | # %% 18 | from sklearn.ensemble import RandomForestRegressor 19 | 20 | clf = RandomForestRegressor() 21 | clf.fit(X_train, y_train) 22 | clf.score(X_test, y_test) -------------------------------------------------------------------------------- /Ch06/06_04/preprocess.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import fetch_california_housing 3 | 4 | cal_housing = fetch_california_housing(as_frame=True) 5 | df = cal_housing['data'] 6 | df.describe() 7 | 8 | # %% 9 | from sklearn.model_selection import train_test_split 10 | 11 | X, y = cal_housing['data'], cal_housing['target'] 12 | X_train, X_test, y_train, y_test = train_test_split( 13 | X, y, 14 | test_size=0.3, 15 | ) 16 | 17 | # %% 18 | from sklearn.svm import SVR 19 | 20 | clf = SVR() 21 | clf.fit(X_train, y_train) 22 | clf.score(X_test, y_test) 23 | 24 | # %% 25 | from sklearn import preprocessing 26 | 27 | X_scaled = preprocessing.scale(X) 28 | 29 | # %% 30 | import pandas as pd 31 | 32 | df = pd.DataFrame( 33 | X_scaled, 34 | columns=cal_housing['feature_names'] 35 | ) 36 | df.describe() 37 | 38 | # %% 39 | 40 | X_train, X_test, y_train, y_test = train_test_split( 41 | X_scaled, y, 42 | test_size=0.3, 43 | ) 44 | clf = SVR() 45 | clf.fit(X_train, y_train) 46 | clf.score(X_test, y_test) 47 | 48 | # %% 49 | from sklearn.decomposition import PCA 50 | 51 | pca = PCA(n_components=4) 52 | X_pca = pca.fit_transform(X) 53 | X_pca.shape -------------------------------------------------------------------------------- /Ch06/06_05/pipe.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import fetch_california_housing 3 | from sklearn.model_selection import train_test_split 4 | 5 | cal_housing = fetch_california_housing() 6 | 7 | X, y = cal_housing['data'], cal_housing['target'] 8 | X_train, X_test, y_train, y_test = train_test_split( 9 | X, y, 10 | test_size=0.3, 11 | ) 12 | 13 | # %% 14 | from sklearn.decomposition import PCA 15 | from sklearn.pipeline import Pipeline 16 | from sklearn.preprocessing import StandardScaler 17 | from sklearn.svm import SVR 18 | 19 | pipe = Pipeline([ 20 | ('scale', StandardScaler()), 21 | ('pca', PCA(n_components=4)), 22 | ('svr', SVR()), 23 | ]) 24 | 25 | 26 | # %% 27 | pipe.fit(X_train, y_train) 28 | pipe.score(X_test, y_test) 29 | 30 | # %% 31 | pipe.steps 32 | 33 | # %% 34 | pipe.get_params() 35 | 36 | # %% 37 | pipe.set_params(svr__C=0.9) -------------------------------------------------------------------------------- /Ch06/06_06/save.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import fetch_california_housing 3 | from sklearn.decomposition import PCA 4 | from sklearn.model_selection import train_test_split 5 | from sklearn.pipeline import Pipeline 6 | from sklearn.preprocessing import StandardScaler 7 | from sklearn.svm import SVR 8 | 9 | cal_housing = fetch_california_housing() 10 | 11 | X, y = cal_housing['data'], cal_housing['target'] 12 | X_train, X_test, y_train, y_test = train_test_split( 13 | X, y, 14 | test_size=0.3, 15 | ) 16 | 17 | pipe = Pipeline([ 18 | ('scale', StandardScaler()), 19 | ('pca', PCA(n_components=4)), 20 | ('svr', SVR()), 21 | ]) 22 | 23 | pipe.fit(X_train, y_train) 24 | pipe.score(X_test, y_test) 25 | 26 | # %% 27 | import pickle 28 | 29 | out_file = 'model.pkl' 30 | 31 | with open(out_file, 'wb') as out: 32 | pickle.dump(pipe, out) 33 | 34 | # %% 35 | with open(out_file, 'rb') as fp: 36 | pipe1 = pickle.load(fp) 37 | pipe1.score(X_test, y_test) -------------------------------------------------------------------------------- /Ch06/challenge/digits.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import load_digits 3 | 4 | digits = load_digits() 5 | X, y = digits['data'], digits['target'] 6 | 7 | # %% 8 | import matplotlib.pyplot as plt 9 | 10 | i = 353 11 | print(y[i]) 12 | img = digits['images'][i] 13 | plt.imshow(img, cmap='gray') 14 | 15 | # %% 16 | img.shape 17 | 18 | # %% 19 | X.shape 20 | 21 | # %% 22 | 23 | """ 24 | Write a pipeline that will learn to predict digits. 25 | It should reduce the number of features to 10 and use a KNeighborsClassifier. 26 | 27 | Split the data to train and test, and answer: 28 | - What is the score of the pipeline on the test data? 29 | - What is the size (in kb) of the serialized pipeline? 30 | """ -------------------------------------------------------------------------------- /Ch06/solution/digits.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import load_digits 3 | 4 | digits = load_digits() 5 | X, y = digits['data'], digits['target'] 6 | # %% 7 | import matplotlib.pyplot as plt 8 | 9 | i = 353 10 | print(y[i]) 11 | img = digits['images'][i] 12 | plt.imshow(img, cmap='gray') 13 | 14 | # %% 15 | img.shape 16 | 17 | # %% 18 | X.shape 19 | 20 | """ 21 | Write a pipeline that will learn to predict digits. 22 | It should reduce the number of features to 10 and use a KNeighborsClassifier. 23 | 24 | Split the data to train and test, and answer: 25 | - What is the score of the pipeline on the test data? 26 | - What is the size (in kb) of the serialized pipeline? 27 | """ 28 | 29 | # %% 30 | 31 | from sklearn.decomposition import PCA 32 | from sklearn.model_selection import train_test_split 33 | from sklearn.pipeline import Pipeline 34 | from sklearn.neighbors import KNeighborsClassifier 35 | 36 | pipe = Pipeline([ 37 | ('pca', PCA(n_components=10)), 38 | ('clf', KNeighborsClassifier()), 39 | ]) 40 | 41 | X_train, X_test, y_train, y_test = train_test_split( 42 | X, y, 43 | test_size=0.2, 44 | ) 45 | pipe.fit(X_train, y_train) 46 | pipe.score(X_test, y_test) 47 | 48 | # %% 49 | import pickle 50 | 51 | kb = 2**10 52 | 53 | data = pickle.dumps(pipe) 54 | len(data)/kb -------------------------------------------------------------------------------- /Ch07/07_02/style.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import matplotlib.pyplot as plt 3 | import numpy as np 4 | 5 | xs = np.linspace(-6, 6, 100) 6 | ys = np.sinc(xs) 7 | plt.plot(xs, ys) 8 | 9 | # %% 10 | plt.style.available 11 | 12 | # %% 13 | plt.style.use('seaborn-whitegrid') 14 | plt.plot(xs, ys) 15 | 16 | # %% 17 | plt.style.use('fivethirtyeight') 18 | plt.plot(xs, ys) -------------------------------------------------------------------------------- /Ch07/07_03/pd_style.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import load_iris 3 | 4 | iris = load_iris(as_frame=True) 5 | df = iris['data'].head(5) 6 | df 7 | 8 | # %% 9 | df.style.highlight_max() 10 | 11 | # %% 12 | df.style.background_gradient() 13 | 14 | # %% 15 | df.style.bar() 16 | 17 | # %% 18 | def color_round(val): 19 | if round(val) == val: 20 | return 'color: blue' 21 | return None 22 | 23 | df.style.applymap(color_round) 24 | 25 | 26 | # %% 27 | def color_odd(val): 28 | if int(val) % 2 == 1: 29 | return 'background-color: orange' 30 | return '' 31 | 32 | df.style.applymap(color_round).applymap(color_odd) 33 | 34 | # %% 35 | df.style.applymap( 36 | color_odd, 37 | subset=df.columns[:2], 38 | ) 39 | 40 | # %% 41 | def top50(col): 42 | is_top50 = col >= col.median() 43 | return [ 44 | 'font-weight: bold' if v else '' 45 | for v in is_top50 46 | ] 47 | 48 | df.style.apply(top50) 49 | -------------------------------------------------------------------------------- /Ch07/07_04/download_data.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | from urllib.request import urlopen 3 | import pandas as pd 4 | 5 | here = Path(__file__).absolute().parent 6 | csv_file = here / 'stocks.csv' 7 | 8 | dfs = [] 9 | symbols = [ 10 | 'GOOG', 11 | 'MSFT', 12 | 'ORCL', 13 | ] 14 | for sym in symbols: 15 | print(f'downloading {sym}') 16 | url = f'https://query1.finance.yahoo.com/v7/finance/download/{sym}' 17 | url += '?period1=1577836800&period2=1609372800&interval=1d&events=history' 18 | with urlopen(url) as resp: 19 | df = pd.read_csv(resp, parse_dates=['Date']) 20 | df['Symbol'] = sym 21 | df.insert(1, 'Symbol', df.pop('Symbol')) 22 | dfs.append(df) 23 | df = pd.concat(dfs) 24 | df.sort_values('Date', inplace=True) 25 | df.to_csv(csv_file, index=False) -------------------------------------------------------------------------------- /Ch07/07_04/pd_plot.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | df = pd.read_csv( 5 | 'stocks.csv', 6 | parse_dates=['Date'], 7 | index_col='Date' 8 | ) 9 | df.head() 10 | 11 | # %% 12 | msft = df.query('Symbol == "MSFT"') 13 | msft['Close'].plot() 14 | 15 | # %% 16 | msft['Close'].plot.kde() 17 | 18 | # %% 19 | ( 20 | df 21 | .pivot( 22 | columns='Symbol', 23 | values='Volume' 24 | ) 25 | .resample('M') 26 | .sum() 27 | .plot.bar(xticks=range(12), rot=0) 28 | ) -------------------------------------------------------------------------------- /Ch07/07_05/download_data.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | from urllib.request import urlopen 3 | import pandas as pd 4 | 5 | here = Path(__file__).absolute().parent 6 | csv_file = here / 'stocks.csv' 7 | 8 | dfs = [] 9 | symbols = [ 10 | 'AAPL', 11 | 'MSFT', 12 | 'ORCL', 13 | ] 14 | for sym in symbols: 15 | print(f'downloading {sym}') 16 | url = f'https://query1.finance.yahoo.com/v7/finance/download/{sym}' 17 | url += '?period1=1577836800&period2=1609372800&interval=1d&events=history' 18 | with urlopen(url) as resp: 19 | df = pd.read_csv(resp, parse_dates=['Date']) 20 | df['Symbol'] = sym 21 | df.insert(1, 'Symbol', df.pop('Symbol')) 22 | dfs.append(df) 23 | df = pd.concat(dfs) 24 | df.sort_values('Date', inplace=True) 25 | df.to_csv(csv_file, index=False) -------------------------------------------------------------------------------- /Ch07/07_05/pd_mpl.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | df = pd.read_csv( 5 | 'stocks.csv', 6 | parse_dates=['Date'], 7 | index_col='Date' 8 | ) 9 | df.head() 10 | 11 | # %% 12 | msft = df.query('Symbol == "MSFT"') 13 | 14 | # %% 15 | daily = ( 16 | msft 17 | ['Volume'] 18 | .groupby(msft.index.weekday) 19 | .median() 20 | ) 21 | daily.plot.bar() 22 | 23 | # %% 24 | ax = daily.plot.bar( 25 | title='MSFT', 26 | rot=0, 27 | ) 28 | ax.set_xticklabels( 29 | ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'] 30 | ) 31 | ax.set_xlabel('Weekday') 32 | ax.set_ylabel('Volume') -------------------------------------------------------------------------------- /Ch07/07_06/interactive.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import matplotlib.pyplot as plt 3 | import numpy as np 4 | 5 | def plot_sin(limit): 6 | xs = np.linspace(-limit, limit, 100) 7 | plt.plot(xs, np.sin(xs), label='sin(x) [-%s - %s]' % (limit, limit)) 8 | 9 | plot_sin(6) 10 | 11 | # %% 12 | from ipywidgets import interact 13 | 14 | @interact(limit=6) 15 | def plot_sin(limit): 16 | xs = np.linspace(-limit, limit, 100) 17 | plt.plot(xs, np.sin(xs), label='sin(x) [-%s - %s]' % (limit, limit)) 18 | 19 | # %% 20 | from ipywidgets import IntSlider 21 | 22 | @interact(limit=IntSlider(min=0, max=20)) 23 | def plot_sin(limit): 24 | xs = np.linspace(-limit, limit, 100) 25 | plt.plot(xs, np.sin(xs), label='sin(x) [-%s - %s]' % (limit, limit)) 26 | -------------------------------------------------------------------------------- /Ch07/07_07/other.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import pandas as pd 3 | 4 | df = pd.read_csv( 5 | 'track.csv', 6 | parse_dates=['time'], 7 | index_col='time' 8 | ) 9 | df = ( 10 | df 11 | .resample('3min') 12 | .mean() 13 | .reset_index() 14 | ) 15 | df.head() 16 | 17 | # %% 18 | import plotly.express as px 19 | 20 | fig = px.bar( 21 | df, 22 | x='time', 23 | y='height', 24 | ) 25 | fig 26 | 27 | # %% 28 | fig.write_html('track.html') -------------------------------------------------------------------------------- /Ch07/challenge/chart.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LinkedInLearning/data-science-foundations-python-scientific-stack-3084641/cb2d7702e66ff3e80816d9f49844c80c18cfa0ba/Ch07/challenge/chart.png -------------------------------------------------------------------------------- /Ch07/challenge/download_data.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | from urllib.request import urlopen 3 | import pandas as pd 4 | 5 | here = Path(__file__).absolute().parent 6 | csv_file = here / 'stocks.csv' 7 | 8 | dfs = [] 9 | symbols = [ 10 | 'GOOG', 11 | 'MSFT', 12 | 'ORCL', 13 | ] 14 | for sym in symbols: 15 | print(f'downloading {sym}') 16 | url = f'https://query1.finance.yahoo.com/v7/finance/download/{sym}' 17 | url += '?period1=1577836800&period2=1609372800&interval=1d&events=history' 18 | with urlopen(url) as resp: 19 | df = pd.read_csv(resp, parse_dates=['Date']) 20 | df['Symbol'] = sym 21 | df.insert(1, 'Symbol', df.pop('Symbol')) 22 | dfs.append(df) 23 | df = pd.concat(dfs) 24 | df.sort_values('Date', inplace=True) 25 | df.to_csv(csv_file, index=False) -------------------------------------------------------------------------------- /Ch07/challenge/montly_close.py: -------------------------------------------------------------------------------- 1 | """ 2 | Using the stock data from stocks.csv, draw a bar chart were: 3 | - The x axis is the month 4 | - The y axis is the median closing price 5 | - Each stock should have it's own chart 6 | """ 7 | -------------------------------------------------------------------------------- /Ch07/solution/download_data.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | from urllib.request import urlopen 3 | import pandas as pd 4 | 5 | here = Path(__file__).absolute().parent 6 | csv_file = here / 'stocks.csv' 7 | 8 | dfs = [] 9 | symbols = [ 10 | 'GOOG', 11 | 'MSFT', 12 | 'ORCL', 13 | ] 14 | for sym in symbols: 15 | print(f'downloading {sym}') 16 | url = f'https://query1.finance.yahoo.com/v7/finance/download/{sym}' 17 | url += '?period1=1577836800&period2=1609372800&interval=1d&events=history' 18 | with urlopen(url) as resp: 19 | df = pd.read_csv(resp, parse_dates=['Date']) 20 | df['Symbol'] = sym 21 | df.insert(1, 'Symbol', df.pop('Symbol')) 22 | dfs.append(df) 23 | df = pd.concat(dfs) 24 | df.sort_values('Date', inplace=True) 25 | df.to_csv(csv_file, index=False) -------------------------------------------------------------------------------- /Ch07/solution/montly_close.py: -------------------------------------------------------------------------------- 1 | """ 2 | Using the stock data from stocks.csv, draw a bar chart were: 3 | - The x axis is the month 4 | - The y axis is the median closing price 5 | - Each stock should have it's own chart 6 | """ 7 | # %% 8 | import pandas as pd 9 | 10 | df = pd.read_csv( 11 | 'stocks.csv', 12 | parse_dates=['Date'], 13 | ) 14 | df.head() 15 | 16 | # %% 17 | df['Month'] = df['Date'].dt.month 18 | 19 | mdf = df.pivot_table( 20 | columns='Symbol', 21 | index='Month', 22 | values='Close', 23 | aggfunc='median' 24 | ) 25 | mdf 26 | 27 | # %% 28 | from calendar import month_abbr 29 | 30 | ax = mdf.plot.bar( 31 | title='Median Monthly Close', 32 | rot=0, 33 | ) 34 | ax.set_xticklabels( 35 | [month_abbr[i+1] for i in ax.get_xticks()] 36 | ) 37 | ax.set_ylabel('Closing Price') -------------------------------------------------------------------------------- /Ch08/08_02/polysum.py: -------------------------------------------------------------------------------- 1 | # %% 2 | def polyn(n): 3 | total = 0 4 | for _ in range(n): 5 | total += (7*n*n) + (-3*n) + 42 6 | return total 7 | 8 | %timeit -n 10_000 polyn(1000) 9 | 10 | # %% 11 | import numba 12 | 13 | @numba.jit 14 | def polyn_jit(n): 15 | total = 0 16 | for _ in range(n): 17 | total += (7*n*n) + (-3*n) + 42 18 | return total 19 | 20 | %timeit -n 10_000 polyn_jit(1000) 21 | -------------------------------------------------------------------------------- /Ch08/08_03/digits.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import load_digits 3 | 4 | digits = load_digits() 5 | 6 | # %% 7 | import matplotlib.pyplot as plt 8 | 9 | idx = 37 10 | plt.imshow(digits['images'][idx], cmap=plt.cm.gray) 11 | 12 | # %% 13 | digits['target'][idx] 14 | 15 | # %% 16 | digits['images'].shape, digits['data'].shape 17 | 18 | # %% 19 | from tensorflow.keras.utils import to_categorical 20 | 21 | to_categorical([0, 1, 2, 0, 1]) 22 | 23 | # %% 24 | X = digits['data'] 25 | y = to_categorical(digits['target']) 26 | 27 | # %% 28 | from sklearn.model_selection import train_test_split 29 | 30 | X_train, X_test, y_train, y_test = \ 31 | train_test_split(X, y, test_size=0.3) 32 | 33 | # %% 34 | from tensorflow.keras import Sequential 35 | from tensorflow.keras.layers import Dense, Activation 36 | 37 | in_dim = X.shape[1] 38 | out_dim = y.shape[1] 39 | 40 | model = Sequential() 41 | model.add(Dense(128, input_shape=(in_dim,))) 42 | model.add(Activation('relu')) 43 | model.add(Dense(out_dim)) 44 | model.add(Activation('sigmoid')) 45 | model.compile( 46 | loss='categorical_crossentropy', 47 | optimizer='adam', 48 | metrics=['accuracy'], 49 | ) 50 | 51 | # %% 52 | model.fit(X_train, y_train, epochs=10) 53 | 54 | # %% 55 | _, accuracy = model.evaluate(X_test, y_test) 56 | accuracy 57 | 58 | # %% 59 | model.predict(X_test[:3]) 60 | 61 | # %% 62 | model.predict(X_test[:3]).argmax(axis=1) 63 | 64 | # %% 65 | y_test[:3].argmax(axis=1) 66 | 67 | # %% 68 | model.save('digits.h5') 69 | # %% 70 | from tensorflow.keras.models import load_model 71 | 72 | model1 = load_model('digits.h5') 73 | model1.predict(X_test[:3]).argmax(axis=1) 74 | -------------------------------------------------------------------------------- /Ch08/08_04/images.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import cv2 3 | 4 | img = cv2.imread('sign.jpg') 5 | img.shape 6 | 7 | # %% 8 | import matplotlib.pyplot as plt 9 | 10 | plt.imshow(img) 11 | 12 | # %% 13 | mpl_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 14 | plt.imshow(mpl_img) 15 | 16 | # %% 17 | gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 18 | plt.imshow(gray) 19 | 20 | # %% 21 | plt.imshow(gray, cmap='gray') 22 | 23 | # %% 24 | edges = cv2.Canny(gray, 80, 150) 25 | plt.imshow(edges, cmap='gray') -------------------------------------------------------------------------------- /Ch08/08_04/sign.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/LinkedInLearning/data-science-foundations-python-scientific-stack-3084641/cb2d7702e66ff3e80816d9f49844c80c18cfa0ba/Ch08/08_04/sign.jpg -------------------------------------------------------------------------------- /Ch08/08_05/nlp.py: -------------------------------------------------------------------------------- 1 | # %% 2 | from sklearn.datasets import fetch_20newsgroups 3 | 4 | corpus = fetch_20newsgroups( 5 | categories=['sci.space'], 6 | remove=['headers', 'footers'], 7 | ) 8 | text = corpus['data'][4] 9 | print(text) 10 | 11 | # %% 12 | import spacy 13 | 14 | nlp = spacy.load("en_core_web_sm") 15 | doc = nlp(text) 16 | doc 17 | 18 | # %% 19 | type(doc) 20 | 21 | # %% 22 | sent = list(doc.sents)[1] 23 | print(sent) 24 | 25 | # %% 26 | for tok in sent: 27 | print(f'{tok.text!r} -> {tok.tag_}') 28 | 29 | # %% 30 | for ent in doc.ents: 31 | print(f'{ent.text} ({ent.label_})') 32 | -------------------------------------------------------------------------------- /Ch08/08_06/big.py: -------------------------------------------------------------------------------- 1 | # %% 2 | import sqlite3 3 | 4 | import pandas as pd 5 | from tqdm import tqdm 6 | 7 | time_cols = [ 8 | 'tpep_pickup_datetime', 9 | 'tpep_dropoff_datetime', 10 | ] 11 | 12 | with sqlite3.connect('taxi.db') as conn: 13 | chunks = pd.read_csv( 14 | 'taxi.csv', 15 | parse_dates=time_cols, 16 | chunksize=100_000) 17 | 18 | for chunk in tqdm(chunks): 19 | chunk.to_sql( 20 | 'rides', 21 | conn, 22 | index=False, 23 | if_exists='append', 24 | ) 25 | 26 | # %% 27 | sql = ''' 28 | SELECT 29 | passenger_count, 30 | COUNT(VendorID) AS count 31 | FROM rides 32 | WHERE passenger_count > 1 33 | GROUP BY passenger_count 34 | ''' 35 | 36 | df = pd.read_sql(sql, conn) 37 | df 38 | 39 | # %% 40 | import dask.dataframe as dd 41 | 42 | ddf = dd.read_csv( 43 | 'taxi.csv', 44 | parse_dates=time_cols, 45 | dtype={'VendorID': 'float64'}, 46 | ) 47 | 48 | # %% 49 | ddf['VendorID'].value_counts() 50 | 51 | # %% 52 | ddf['VendorID'].value_counts().compute() 53 | -------------------------------------------------------------------------------- /Ch08/08_06/download_data.py: -------------------------------------------------------------------------------- 1 | """Download 10% of January 2016 taxi data to taxi.csv""" 2 | 3 | from urllib.request import urlopen 4 | from time import monotonic 5 | from pathlib import Path 6 | 7 | here = Path(__file__).absolute().parent 8 | csv_file = here / 'taxi.csv' 9 | url = 'https://s3.amazonaws.com/nyc-tlc/csv_backup/yellow_tripdata_2016-01.csv' 10 | out_name = '/'.join(csv_file.parts[-3:]) 11 | 12 | start = monotonic() 13 | with urlopen(url) as resp: 14 | payload_size = int(resp.headers['Content-Length']) 15 | size_mb = payload_size / (2**20) 16 | print(f'downloading 10% of {size_mb:.2f}MB to {out_name}') 17 | size = 0 18 | with open(csv_file, 'wb') as out: 19 | for i, line in enumerate(resp): 20 | size += len(line) 21 | prec = (size / payload_size) * 100 22 | print(f' {prec:.2f}%', end='\r') 23 | if i % 10 != 0: 24 | continue 25 | out.write(line) 26 | 27 | if size != payload_size: 28 | raise SystemError(f'error: expected {payload_size} bytes, got {size}') 29 | 30 | duration = int(monotonic() - start) 31 | print(f'{csv_file}: {size_mb:.1f}MB downloaded in {duration} seconds') 32 | -------------------------------------------------------------------------------- /Ch09/09_05/scale.py: -------------------------------------------------------------------------------- 1 | def scale(vec, n): 2 | return vec * n -------------------------------------------------------------------------------- /Ch09/09_05/test_scale.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from scale import scale 4 | 5 | 6 | def test_simple(): 7 | v = np.array([0.1, np.nan, 1.1]) 8 | n = 1.1 9 | expected = np.array([0.11, np.nan, 1.21]) 10 | out = scale(v, n) 11 | assert np.allclose(expected, out, equal_nan=True) -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | LinkedIn Learning Exercise Files License Agreement 2 | ================================================== 3 | 4 | This License Agreement (the "Agreement") is a binding legal agreement 5 | between you (as an individual or entity, as applicable) and LinkedIn 6 | Corporation (“LinkedIn”). 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IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 89 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 90 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 91 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 92 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 93 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 94 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE." 95 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Data Science Foundations: Python Scientific Stack 2 | This is the repository for the LinkedIn Learning course Data Science Foundations: Python Scientific Stack. The full course is available from [LinkedIn Learning][lil-course-url]. 3 | 4 | ![1667582946722](https://user-images.githubusercontent.com/28540243/200747253-46f086c1-eb70-4d23-b8d5-4d0c0b8afb4a.jpeg) 5 | 6 | Join instructor Miki Tebeka as he dives into the Python scientific stack and shows you how to use it to solve problems. Miki covers the major packages used throughout the data science process: numpy, pandas, matplotlib, scikit-learn, and others. He also guides you through how to load data, analyze data, run models, and display results.

This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the [Using GitHub Codespaces with this course][gcs-video-url] video to learn how to get started. 7 | 8 | ### Instructor 9 | 10 | Miki Tebeka 11 | 12 | Check out my other courses on [LinkedIn Learning](https://www.linkedin.com/learning/instructors/miki-tebeka?u=104). 13 | 14 | [lil-course-url]: https://www.linkedin.com/learning/data-science-foundations-python-scientific-stack-17064277 15 | [lil-thumbnail-url]: https://media.licdn.com/dms/image/D560DAQG2_xrJ2bSZsA/learning-public-crop_675_1200/0/1667582946722?e=1668438000&v=beta&t=T8nn2PpmZ1gbAf_3mtvdR-lktV-czyNa01SrtWbWoMY 16 | [gcs-video-url]: https://www.linkedin.com/learning/data-science-foundations-python-scientific-stack-17064277/using-github-codespaces-with-this-course 17 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | dask~=2022.8 2 | folium~=0.12 3 | ipywidgets~=8.0 4 | matplotlib~=3.5 5 | numba~=0.56 6 | opencv-python~=4.6 7 | pandas~=1.4 8 | plotly~=5.10 9 | pyarrow~=14.0 # Required to read parquet 10 | pytest~=7.1 11 | scikit-learn~=1.1 12 | scipy~=1.9 13 | shapely~=1.8 14 | spacy~=3.4 15 | tensorflow~=2.9 16 | --------------------------------------------------------------------------------