This animation demonstrates the central limit theorem for a uniform sampling distribution.
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| // [START apps_script_bigquery_update_sheet] | |
| /** | |
| * Runs a BigQuery query and replace the existing sheet | |
| */ | |
| /** | |
| * Add a custom menu to the spreadsheet when it is opened. | |
| */ | |
| function onOpen() { | |
| var spreadsheet = SpreadsheetApp.getActive(); |
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| /** | |
| * Fetching data from BigQuery and present it in our sheet | |
| * Author: Ido Green | |
| * Date: 14/12/2013 | |
| * | |
| * See: http://wp.me/pB1lQ-19i | |
| * Misc: https://developers.google.com/bigquery/ | |
| */ | |
| // |
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| """Example to show how to plot histogram with accumulate ratio. | |
| Plot multiple histogram using seaborn.FacetGrid() | |
| """ | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| % matplotlib inline |
- Update HISTORY.rst
- Commit the changes:
git add HISTORY.rst
git commit -m "Changelog for upcoming release 0.1.1."
- Update version number (can also be minor or major)
bumpversion patch
SQL to MongoDB Mapping Chart¶
In addition to the charts that follow, you might want to consider the Frequently Asked Questions section for a selection of common questions about MongoDB.
Executables¶
The following table presents the MySQL/Oracle executables and the corresponding MongoDB executables.
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| # default.custom.yaml | |
| # save it to: | |
| # ~/.config/ibus/rime (linux) | |
| # ~/Library/Rime (macos) | |
| # %APPDATA%\Rime (windows) | |
| patch: | |
| schema_list: | |
| - schema: luna_pinyin # 朙月拼音 | |
| - schema: luna_pinyin_simp # 朙月拼音 简化字模式 |
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| 编号 代码 命令分解 命令 | |
| //switchtabpos switch tab pos 更改底部功能按钮位置的命令 | |
| //multiwebview multi web view 微信多窗口显示命令 | |
| //opentrace open trace 打开跟踪 | |
| //getfpkey get fp key 得到手机基本信息 | |
| //pickpoi pick poi 定位当前位置 | |
| //fullexit full exit 完全退出微信 | |
| //testwaitsms test wait sms 测试验证手机号码 | |
| //sightinfo sight info 打开查看小视频参数 | |
| //testsetpageowner test set page owner 检测你是否拥有页面的所有权 |
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| Result: 1 | |
| Items { | |
| TemplateId: "BADGE_BATTLE_ATTACK_WON" | |
| Badge { | |
| BadgeType: BADGE_BATTLE_ATTACK_WON | |
| BadgeRanks: 4 | |
| Targets: "\nd\350\007" | |
| } | |
| } | |
| Items { |
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| ''' | |
| Most heatmap tutorials I found online use pyplot.pcolormesh with random sets of | |
| data from Numpy; I just needed to plot x, y, z values stored in lists--without | |
| all the Numpy mumbo jumbo. Here I have code to plot intensity on a 2D array, and | |
| I only use Numpy where I need to (pcolormesh expects Numpy arrays as inputs). | |
| ''' | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| #here's our data to plot, all normal Python lists |