| Instance | Branch |
|---|
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| package main | |
| import "fmt" | |
| func main() { | |
| var a []int | |
| a = make([]int, 5) | |
| for i := 0; i < 5; i++ { | |
| a = append(a, i) | |
| fmt.Println(a) |
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| package main | |
| import "fmt" | |
| func main() { | |
| n := make([]int, 4, 5) | |
| fmt.Printf("len=%d, cap=%d, slice=%v\n", len(n), cap(n), n) | |
| n = append(n, 1) | |
| fmt.Printf("len=%d, cap=%d, slice=%v\n", len(n), cap(n), n) | |
| n = append(n, 2) |
ekusiadadus
/ main.py
Last active
October 31, 2022 08:18
Twitter を BigQuery と JupyterLab で分析してみた ~ Twitter API v2 ~
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| import requests | |
| import os | |
| import urllib | |
| import json | |
| import datetime | |
| from google.cloud import bigquery | |
| bearer_token = "[Your Token]" | |
| headers = {"Authorization": "Bearer {}".format(bearer_token)} |
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| { | |
| "data": [ | |
| { | |
| "possibly_sensitive": false, | |
| "lang": "ja", | |
| "source": "Twitter Web App", | |
| "created_at": "2022-10-30T15:16:28.000Z", | |
| "author_id": "1299285051436273664", | |
| "entities": { | |
| "urls": [ |
ekusiadadus
/ introduction_to_numerical_optimal_control.ipynb
Created
May 25, 2022 13:16
— forked from mayataka/introduction_to_numerical_optimal_control.ipynb
数値最適制御入門の入門
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ekusiadadus
/ gist:daf978e822aa8ab0490436118f3d68a8
Created
August 12, 2021 11:20
— forked from digitaljhelms/gist:4287848
Git/GitHub branching standards & conventions
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