Explore all Bitcoin open source software, libraries, packages, source code, cloud functions and APIs.
Popular New Releases in Bitcoin
bitcoin
Bitcoin Core 22.0
freqtrade
2022.3
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v1.7.3
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Dogecoin Core 1.14.4
chia-blockchain
1.3.4
Popular Libraries in Bitcoin
bitcoin
by bitcoin 
c++
63479 MIT
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ccxt
by ccxt javascript
24082 MIT
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freqtrade
by freqtrade python
17102 GPL-3.0
Free, open source crypto trading bot
ZeroNet
by HelloZeroNet javascript
16995 NOASSERTION
ZeroNet - Decentralized websites using Bitcoin crypto and BitTorrent network
bitcoinbook
by bitcoinbook python
16943 NOASSERTION
Mastering Bitcoin 2nd Edition - Programming the Open Blockchain
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by chaozh javascript
15698 Apache-2.0
收集所有区块链(BlockChain)技术开发相关资料，包括Fabric和Ethereum开发资料
web3.js
by ChainSafe javascript
14383 NOASSERTION
Ethereum JavaScript API
dogecoin
by dogecoin c++
12895 MIT
very currency
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by Chia-Network python
10750 Apache-2.0
Chia blockchain python implementation (full node, farmer, harvester, timelord, and wallet)
Trending New libraries in Bitcoin
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by GamestonkTerminal python
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Investment Research for Everyone.
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Automated Binance trading bot - Trade multiple cryptocurrencies. Buy low/sell high with Grid Trading. Integrated with TradingView technical analysis
plow
by six-ddc go
2938 Apache-2.0
A high-performance HTTP benchmarking tool with real-time web UI and terminal displaying
Binance-volatility-trading-bot
by CyberPunkMetalHead python
2797 MIT
This is a fully functioning Binance trading bot that measures the volatility of every coin on Binance and places trades with the highest gaining coins If you like this project consider donating though the Brave browser to allow me to continuously improve the script.
umbrel
by getumbrel html
1659 NOASSERTION
A beautiful personal server OS for Raspberry Pi or any Linux distro for running self-hosted apps
avalanchego
by ava-labs go
1535 BSD-3-Clause
Go implementation of an Avalanche node.
Binance-News-Sentiment-Bot
by CyberPunkMetalHead python
1220 MIT
This is a fully functioning Binance trading bot that takes into account the news sentiment for the top 100 crypto feeds. If you like this project consider donating though the Brave browser to allow me to continuously improve the script.
NostalgiaForInfinity
by iterativv python
1132 GPL-3.0
Trading strategy for the Freqtrade crypto bot
mobilecoin
by mobilecoinfoundation rust
1057 NOASSERTION
Private payments for mobile devices.
Top Authors in Bitcoin
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1
42 Libraries
12616
2
26 Libraries
6311
3
22 Libraries
4224
4
21 Libraries
4815
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17 Libraries
3446
6
17 Libraries
909
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16 Libraries
271
8
16 Libraries
408
9
15 Libraries
4331
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308
Trending Kits in Bitcoin
No Trending Kits are available at this moment for Bitcoin
Trending Discussions on Bitcoin
Flutter - How to refresh a Widget on button Click?
Adjusting Labels of Altair Grouped Bar Chart
different htaccess for directory issue
Add data into 2 columns in same row with the data coming from Javascipt Websocket
How to resample frequency conditional on two columns in pandas?
How to fix LevelDB library load error when running RSKj node on a Windows machine?
after updating flutter, Firebase.initializeApp() does not seem to work on my phone anymore
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Calculating Bitcoin mining difficulty
Python - BeautifulSoup - Selecting a 'div' with 'class'-attribute shows every div in the html
QUESTION
Flutter - How to refresh a Widget on button Click?
Asked 2022-Mar-01 at 10:15I'm making an app where I plot charts of cryptocurrency. I have a file like this, that renders a chart of btc/24h in it. I'm using chart_sparkline package to plot the chart.
I have the following code, and it is not working on a button click, how do I fix this?
1import 'package:flutter/material.dart';
2import 'package:tracker/renderchart.dart';
3
4class Portfolio extends StatefulWidget {
5 @override
6 _PortfolioState createState() => _PortfolioState();
7}
8
9class _PortfolioState extends State<Portfolio> {
10 Widget portfolioChart = RenderPortfolioChart(coin: "bitcoin", days: 1);
11
12 @override
13 Widget build(BuildContext context) {
14 return Container(
15 child: Column(
16 children: [
17 portfolioChart,
18
19 const Padding(padding: EdgeInsets.only(top: 10.0)),
20
21 const Text("Hello, there!", style: TextStyle(color: Colors.white)),
22
23 const Padding(padding: EdgeInsets.only(top: 10.0)),
24
25 ElevatedButton(
26 onPressed: (){
27 print("updating chart");
28 setState(() {
29 portfolioChart = RenderPortfolioChart(coin: "ethereum", days: 1);
30 });
31 },
32
33 child: Text("ETH"),
34 )
35 ]
36 ),
37 );
38 }
39}
40Basically, the elevatedButton should update the chart, but it is not working in my case, how to fix this?
ANSWER
Answered 2022-Mar-01 at 10:15I fixed this, I was passing constructor parameters of Portfolio to _PortfolioState, this won't update the values the next time. Instead of passing these values to _PortfolioState, I used widget.coin and widget.days to extract the data from the Portfolio class.
Thanks to this StackOverflow link: Passing Data to a Stateful Widget in Flutter
QUESTION
Adjusting Labels of Altair Grouped Bar Chart
Asked 2022-Jan-25 at 00:04I have this dataframe called table:
1TERM Bitcoin S&P500 Real Estate Gold
20 High-Inflation/ Short term 3097.94 -3700.78 761.23 6512.71
31 High-Inflation/ Mid term — -3080.01 -8434.66 3242.40
42 High-Inflation/ Long term — -2089.25 -9117.96 8174.43
53 Low-Inflation/ Short term 780200.00 -273.71 1824.72 2214.51
64 Low-Inflation/ Mid term 21013600.00 5331.40 35810.58 -2879.37
75 Low-Inflation/ Long term 978017143.00. 15045.41 35895.81 861.90
8And have generated a graph with this code:
1TERM Bitcoin S&P500 Real Estate Gold
20 High-Inflation/ Short term 3097.94 -3700.78 761.23 6512.71
31 High-Inflation/ Mid term — -3080.01 -8434.66 3242.40
42 High-Inflation/ Long term — -2089.25 -9117.96 8174.43
53 Low-Inflation/ Short term 780200.00 -273.71 1824.72 2214.51
64 Low-Inflation/ Mid term 21013600.00 5331.40 35810.58 -2879.37
75 Low-Inflation/ Long term 978017143.00. 15045.41 35895.81 861.90
8from itertools import product
9alt.Chart(table).transform_fold(
10 ["Bitcoin", "S&P500", "Real Estate", "Gold"], as_=["key", "value"]
11).mark_bar().encode(
12 x="key:N",
13 y=alt.Y("value:Q", scale=alt.Scale(type='symlog'),
14 axis=alt.Axis(values=[0] + [10**x * y for (x, y) in product(range(1, 10, 1), (1, -0.01))])),
15 color="key:N",
16 column="TERM",
17)
18However as you can see the labels at the top are smushed together. I am not sure how to make those more visually appealing, and also how to remove the key label on the x-axis of each graph:
ANSWER
Answered 2022-Jan-25 at 00:04You can remove the x title by setting title=None in the x encoding. You can add a line break to the problematic strings by replacing them with a list of lines. For example:
1TERM Bitcoin S&P500 Real Estate Gold
20 High-Inflation/ Short term 3097.94 -3700.78 761.23 6512.71
31 High-Inflation/ Mid term — -3080.01 -8434.66 3242.40
42 High-Inflation/ Long term — -2089.25 -9117.96 8174.43
53 Low-Inflation/ Short term 780200.00 -273.71 1824.72 2214.51
64 Low-Inflation/ Mid term 21013600.00 5331.40 35810.58 -2879.37
75 Low-Inflation/ Long term 978017143.00. 15045.41 35895.81 861.90
8from itertools import product
9alt.Chart(table).transform_fold(
10 ["Bitcoin", "S&P500", "Real Estate", "Gold"], as_=["key", "value"]
11).mark_bar().encode(
12 x="key:N",
13 y=alt.Y("value:Q", scale=alt.Scale(type='symlog'),
14 axis=alt.Axis(values=[0] + [10**x * y for (x, y) in product(range(1, 10, 1), (1, -0.01))])),
15 color="key:N",
16 column="TERM",
17)
18table['TERM'] = table['TERM'].str.split(' ', 1)
19alt.Chart(table).transform_fold(
20 ["Bitcoin", "S&P500", "Real Estate", "Gold"], as_=["key", "value"]
21).mark_bar().encode(
22 x=alt.X("key:N", title=None),
23 y=alt.Y("value:Q", scale=alt.Scale(type='symlog'),
24 axis=alt.Axis(values=[0] + [10**x * y for (x, y) in product(range(1, 10, 1), (1, -0.01))])),
25 color="key:N",
26 column="TERM"
27)
28
QUESTION
different htaccess for directory issue
Asked 2022-Jan-21 at 17:231root
2 /question
3 /pages
4 index.html
5 .htaccess
6/root/.htaccess
1root
2 /question
3 /pages
4 index.html
5 .htaccess
6RewriteEngine On
7RewriteBase /
8
9RewriteCond %{REQUEST_URI} !^/question
10/question/.htaccess
1root
2 /question
3 /pages
4 index.html
5 .htaccess
6RewriteEngine On
7RewriteBase /
8
9RewriteCond %{REQUEST_URI} !^/question
10RewriteEngine On
11#RewriteBase /
12
13RewriteRule ^question/are-bitcoin-gains-taxable/?$ /pages/questions/viewQuestion.php?id=9 [L]
14So what I'm trying to do is separate my htaccess into directories, this current setup is producing /404 error /pages/questions/viewQuestion.php? does exist, I just think its not reading the second .htaccess , Thanks for any help.
ANSWER
Answered 2022-Jan-21 at 17:23You don't need anything in root .htaccess for this and just a RewriteCond by itself isn't doing anything anyway.
You just need to have this rule in /question/.htaccess
1root
2 /question
3 /pages
4 index.html
5 .htaccess
6RewriteEngine On
7RewriteBase /
8
9RewriteCond %{REQUEST_URI} !^/question
10RewriteEngine On
11#RewriteBase /
12
13RewriteRule ^question/are-bitcoin-gains-taxable/?$ /pages/questions/viewQuestion.php?id=9 [L]
14RewriteEngine On
15
16RewriteRule ^are-bitcoin-gains-taxable/?$ /pages/questions/viewQuestion.php?id=9 [L,NC,QSA]
17URI paths in RewriteRule are relative to the directory where .htaccess is located so question/ won't be matched here.
QUESTION
Add data into 2 columns in same row with the data coming from Javascipt Websocket
Asked 2022-Jan-17 at 05:28I am new to JavaScript, not sure if this very basic question. I've created a Bitcoin Price update dashboard using the data fetched from the external WebSocket. I managed to get the data from the WebSocket and display it on the HTML table but i was only able to fetch only the live price of the bitcoin I want to also display the Bitcoin quantity in the other column. I want to know how to append two different data in 2 columns in one row. I tried my best to explain.
I have provided the code snippets below as well as external Websocket from where I am pulling the data.
NOTE: I'm pulling the data from an external websocket and with that WebSocket I can get all the details like Bitcoin price, quantity, type and everything. "messageObject.p" means Price and "messageObject.q" means Quantity. p=price and q=quantity. I'm able to get the messageObject.q (quantity) to be displayed on the table but I'm unable to show the messageObject.p (price) in the other column of the table.
Please let me know how should I insert the row dynamically into a HTML table. Thank you so much in advance
1<table id="tableprice" class="table table-striped">
2 <thead>
3 <tr>
4 <th>Amount(BTC)</th>
5 <th scope="col">Price(USDT)</th>
6 </tr>
7 </thead>
8 <tbody id="pricetable" class="crypt-table-hover">
9 </tbody>
10</table>
11
12<script>
13 window.onload = () => {
14 function insertRow(price){
15 var tr = document.createElement("tr"),
16 tdCoin = document.createElement("td"),
17 tdPrice = document.createElement("td"),
18 docFrag = new DocumentFragment();
19 tdPrice.style.color="#49C279";
20 tdCoin.textContent = `${(price.slice(0)).toLocaleString("en-US")}`;
21 tdPrice.textContent = `${Number(price.slice(0,-6)).toLocaleString("en-US",{style: 'currency', currency: 'USD'})}`;
22 tr.appendChild(tdCoin);
23 tr.appendChild(tdPrice);
24 docFrag.appendChild(tr);
25 return docFrag;
26}
27
28
29var binanceSocket = new WebSocket("wss://stream.binance.com:9443/ws/btcusdt@aggTrade"),
30 table = document.getElementById("pricetable");
31binanceSocket.onmessage = function(event) {
32 var messageObject = JSON.parse(event.data);
33 table.appendChild(insertRow(messageObject.q, messageObject.p));
34
35
36
37 const MAX_ROWS = 16;
38 const rows = table.querySelectorAll("tr");
39 if (rows.length > MAX_ROWS) table.removeChild(rows[0]);
40
41
42}
43
44 </script>
45ANSWER
Answered 2022-Jan-09 at 21:26You only have one parameter in your insertRow() function. Also, inside of your insertRow() function, you are never trying to read from the passed in quantity variable.
QUESTION
How to resample frequency conditional on two columns in pandas?
Asked 2022-Jan-06 at 10:24I have the following dataframe:
1import pandas as pd
2
3
4data_as_dict = {'Date': {0: '2015-01-01 00:00:00', 1: '2015-01-01 00:00:02', 2: '2015-01-01 00:00:02', 3: '2015-01-01 00:00:02', 4: '2015-01-01 00:00:02',
5 5: '2015-01-01 00:00:03', 6: '2015-01-01 00:00:03', 7: '2015-01-01 00:00:04', 8: '2015-01-01 00:00:04', 9: '2015-01-01 00:00:04', 10: '2015-01-01 00:00:05',
6 11: '2015-01-01 00:00:05', 12: '2015-01-01 00:00:05', 13: '2015-01-01 00:00:06', 14: '2015-01-01 00:00:07', 15: '2015-01-01 00:00:07',
7 16: '2015-01-01 00:00:07', 17: '2015-01-01 00:00:08', 18: '2015-01-01 00:00:08', 19: '2015-01-01 00:00:08'}, 'Asset': {0: 'baidu-inc', 1: 'bitcoin', 2: 'bitcoin', 3: 'ftse-100', 4: 'ftse-100', 5: 'baidu-inc', 6: 'bitcoin', 7: 'bitcoin', 8: 'ftse-100', 9: 'baidu-inc', 10: 'baidu-inc', 11: 'ftse-100', 12: 'bitcoin', 13: 'baidu-inc', 14: 'ftse-100', 15: 'bitcoin', 16: 'ftse-100', 17: 'baidu-inc', 18: 'baidu-inc', 19: 'bitcoin'}, 'Class': {0: 'S', 1: 'E', 2: 'E', 3: 'G', 4: 'G', 5: 'S', 6: 'E', 7: 'S', 8: 'G', 9: 'G', 10: 'G', 11: 'S', 12: 'S', 13: 'S', 14: 'S', 15: 'S', 16: 'S', 17: 'E', 18: 'E', 19: 'S'}, 'Score': {0: -0.8674, 1: 0.395, 2: 0.0, 3: -0.3612, 4: 0.5023, 5: 0.0129, 6: -0.5023, 7: 0.0, 8: -0.5023, 9: 0.0, 10: -0.7579, 11: -0.8843, 12: 0.8968, 13: 0.7579, 14: 0.5466, 15: 0.2023, 16: 0.0, 17: 0.6457, 18: 0.0, 19: -0.5023}}
8
9df = pd.DataFrame.from_dict(data_as_dict)
10df['Date'] = pd.to_datetime(df['Date'])
11
12 Date Asset Class Score
130 2015-01-01 00:00:00 baidu-inc S -0.8674
141 2015-01-01 00:00:02 bitcoin E 0.3950
152 2015-01-01 00:00:02 bitcoin E 0.0000
163 2015-01-01 00:00:02 ftse-100 G -0.3612
174 2015-01-01 00:00:02 ftse-100 G 0.5023
185 2015-01-01 00:00:03 baidu-inc S 0.0129
196 2015-01-01 00:00:03 bitcoin E -0.5023
207 2015-01-01 00:00:04 bitcoin S 0.0000
218 2015-01-01 00:00:04 ftse-100 G -0.5023
229 2015-01-01 00:00:04 baidu-inc G 0.0000
2310 2015-01-01 00:00:05 baidu-inc G -0.7579
2411 2015-01-01 00:00:05 ftse-100 S -0.8843
2512 2015-01-01 00:00:05 bitcoin S 0.8968
2613 2015-01-01 00:00:06 baidu-inc S 0.7579
2714 2015-01-01 00:00:07 ftse-100 S 0.5466
2815 2015-01-01 00:00:07 bitcoin S 0.2023
2916 2015-01-01 00:00:07 ftse-100 S 0.0000
3017 2015-01-01 00:00:08 baidu-inc E 0.6457
3118 2015-01-01 00:00:08 baidu-inc E 0.0000
3219 2015-01-01 00:00:08 bitcoin S -0.5023
33
34What I would like to do is to resample the frequency of each 'Asset' for each 'Class' from 1 second to 1 hour. What I tried without success is:
1import pandas as pd
2
3
4data_as_dict = {'Date': {0: '2015-01-01 00:00:00', 1: '2015-01-01 00:00:02', 2: '2015-01-01 00:00:02', 3: '2015-01-01 00:00:02', 4: '2015-01-01 00:00:02',
5 5: '2015-01-01 00:00:03', 6: '2015-01-01 00:00:03', 7: '2015-01-01 00:00:04', 8: '2015-01-01 00:00:04', 9: '2015-01-01 00:00:04', 10: '2015-01-01 00:00:05',
6 11: '2015-01-01 00:00:05', 12: '2015-01-01 00:00:05', 13: '2015-01-01 00:00:06', 14: '2015-01-01 00:00:07', 15: '2015-01-01 00:00:07',
7 16: '2015-01-01 00:00:07', 17: '2015-01-01 00:00:08', 18: '2015-01-01 00:00:08', 19: '2015-01-01 00:00:08'}, 'Asset': {0: 'baidu-inc', 1: 'bitcoin', 2: 'bitcoin', 3: 'ftse-100', 4: 'ftse-100', 5: 'baidu-inc', 6: 'bitcoin', 7: 'bitcoin', 8: 'ftse-100', 9: 'baidu-inc', 10: 'baidu-inc', 11: 'ftse-100', 12: 'bitcoin', 13: 'baidu-inc', 14: 'ftse-100', 15: 'bitcoin', 16: 'ftse-100', 17: 'baidu-inc', 18: 'baidu-inc', 19: 'bitcoin'}, 'Class': {0: 'S', 1: 'E', 2: 'E', 3: 'G', 4: 'G', 5: 'S', 6: 'E', 7: 'S', 8: 'G', 9: 'G', 10: 'G', 11: 'S', 12: 'S', 13: 'S', 14: 'S', 15: 'S', 16: 'S', 17: 'E', 18: 'E', 19: 'S'}, 'Score': {0: -0.8674, 1: 0.395, 2: 0.0, 3: -0.3612, 4: 0.5023, 5: 0.0129, 6: -0.5023, 7: 0.0, 8: -0.5023, 9: 0.0, 10: -0.7579, 11: -0.8843, 12: 0.8968, 13: 0.7579, 14: 0.5466, 15: 0.2023, 16: 0.0, 17: 0.6457, 18: 0.0, 19: -0.5023}}
8
9df = pd.DataFrame.from_dict(data_as_dict)
10df['Date'] = pd.to_datetime(df['Date'])
11
12 Date Asset Class Score
130 2015-01-01 00:00:00 baidu-inc S -0.8674
141 2015-01-01 00:00:02 bitcoin E 0.3950
152 2015-01-01 00:00:02 bitcoin E 0.0000
163 2015-01-01 00:00:02 ftse-100 G -0.3612
174 2015-01-01 00:00:02 ftse-100 G 0.5023
185 2015-01-01 00:00:03 baidu-inc S 0.0129
196 2015-01-01 00:00:03 bitcoin E -0.5023
207 2015-01-01 00:00:04 bitcoin S 0.0000
218 2015-01-01 00:00:04 ftse-100 G -0.5023
229 2015-01-01 00:00:04 baidu-inc G 0.0000
2310 2015-01-01 00:00:05 baidu-inc G -0.7579
2411 2015-01-01 00:00:05 ftse-100 S -0.8843
2512 2015-01-01 00:00:05 bitcoin S 0.8968
2613 2015-01-01 00:00:06 baidu-inc S 0.7579
2714 2015-01-01 00:00:07 ftse-100 S 0.5466
2815 2015-01-01 00:00:07 bitcoin S 0.2023
2916 2015-01-01 00:00:07 ftse-100 S 0.0000
3017 2015-01-01 00:00:08 baidu-inc E 0.6457
3118 2015-01-01 00:00:08 baidu-inc E 0.0000
3219 2015-01-01 00:00:08 bitcoin S -0.5023
33
34df.groupby(['Asset','Class']).set_index('Date').resample('1h').mean()
35
36More in detail, I want to resample the frequency of the asset 'baidu-inc' from 1s to 1h within each class (S, G, and E). The same logic applies to every asset.
Can anyone help me do it?
Thanks a lot!
ANSWER
Answered 2022-Jan-06 at 10:16Rounding and pivot table is a solution:
1import pandas as pd
2
3
4data_as_dict = {'Date': {0: '2015-01-01 00:00:00', 1: '2015-01-01 00:00:02', 2: '2015-01-01 00:00:02', 3: '2015-01-01 00:00:02', 4: '2015-01-01 00:00:02',
5 5: '2015-01-01 00:00:03', 6: '2015-01-01 00:00:03', 7: '2015-01-01 00:00:04', 8: '2015-01-01 00:00:04', 9: '2015-01-01 00:00:04', 10: '2015-01-01 00:00:05',
6 11: '2015-01-01 00:00:05', 12: '2015-01-01 00:00:05', 13: '2015-01-01 00:00:06', 14: '2015-01-01 00:00:07', 15: '2015-01-01 00:00:07',
7 16: '2015-01-01 00:00:07', 17: '2015-01-01 00:00:08', 18: '2015-01-01 00:00:08', 19: '2015-01-01 00:00:08'}, 'Asset': {0: 'baidu-inc', 1: 'bitcoin', 2: 'bitcoin', 3: 'ftse-100', 4: 'ftse-100', 5: 'baidu-inc', 6: 'bitcoin', 7: 'bitcoin', 8: 'ftse-100', 9: 'baidu-inc', 10: 'baidu-inc', 11: 'ftse-100', 12: 'bitcoin', 13: 'baidu-inc', 14: 'ftse-100', 15: 'bitcoin', 16: 'ftse-100', 17: 'baidu-inc', 18: 'baidu-inc', 19: 'bitcoin'}, 'Class': {0: 'S', 1: 'E', 2: 'E', 3: 'G', 4: 'G', 5: 'S', 6: 'E', 7: 'S', 8: 'G', 9: 'G', 10: 'G', 11: 'S', 12: 'S', 13: 'S', 14: 'S', 15: 'S', 16: 'S', 17: 'E', 18: 'E', 19: 'S'}, 'Score': {0: -0.8674, 1: 0.395, 2: 0.0, 3: -0.3612, 4: 0.5023, 5: 0.0129, 6: -0.5023, 7: 0.0, 8: -0.5023, 9: 0.0, 10: -0.7579, 11: -0.8843, 12: 0.8968, 13: 0.7579, 14: 0.5466, 15: 0.2023, 16: 0.0, 17: 0.6457, 18: 0.0, 19: -0.5023}}
8
9df = pd.DataFrame.from_dict(data_as_dict)
10df['Date'] = pd.to_datetime(df['Date'])
11
12 Date Asset Class Score
130 2015-01-01 00:00:00 baidu-inc S -0.8674
141 2015-01-01 00:00:02 bitcoin E 0.3950
152 2015-01-01 00:00:02 bitcoin E 0.0000
163 2015-01-01 00:00:02 ftse-100 G -0.3612
174 2015-01-01 00:00:02 ftse-100 G 0.5023
185 2015-01-01 00:00:03 baidu-inc S 0.0129
196 2015-01-01 00:00:03 bitcoin E -0.5023
207 2015-01-01 00:00:04 bitcoin S 0.0000
218 2015-01-01 00:00:04 ftse-100 G -0.5023
229 2015-01-01 00:00:04 baidu-inc G 0.0000
2310 2015-01-01 00:00:05 baidu-inc G -0.7579
2411 2015-01-01 00:00:05 ftse-100 S -0.8843
2512 2015-01-01 00:00:05 bitcoin S 0.8968
2613 2015-01-01 00:00:06 baidu-inc S 0.7579
2714 2015-01-01 00:00:07 ftse-100 S 0.5466
2815 2015-01-01 00:00:07 bitcoin S 0.2023
2916 2015-01-01 00:00:07 ftse-100 S 0.0000
3017 2015-01-01 00:00:08 baidu-inc E 0.6457
3118 2015-01-01 00:00:08 baidu-inc E 0.0000
3219 2015-01-01 00:00:08 bitcoin S -0.5023
33
34df.groupby(['Asset','Class']).set_index('Date').resample('1h').mean()
35
36df["Date"] = df["Date"].dt.round("1H")
37cross = df.pivot_table(index="Date", columns=["Asset", "Class"], values="Score", aggfunc="mean")
38It returns:
1import pandas as pd
2
3
4data_as_dict = {'Date': {0: '2015-01-01 00:00:00', 1: '2015-01-01 00:00:02', 2: '2015-01-01 00:00:02', 3: '2015-01-01 00:00:02', 4: '2015-01-01 00:00:02',
5 5: '2015-01-01 00:00:03', 6: '2015-01-01 00:00:03', 7: '2015-01-01 00:00:04', 8: '2015-01-01 00:00:04', 9: '2015-01-01 00:00:04', 10: '2015-01-01 00:00:05',
6 11: '2015-01-01 00:00:05', 12: '2015-01-01 00:00:05', 13: '2015-01-01 00:00:06', 14: '2015-01-01 00:00:07', 15: '2015-01-01 00:00:07',
7 16: '2015-01-01 00:00:07', 17: '2015-01-01 00:00:08', 18: '2015-01-01 00:00:08', 19: '2015-01-01 00:00:08'}, 'Asset': {0: 'baidu-inc', 1: 'bitcoin', 2: 'bitcoin', 3: 'ftse-100', 4: 'ftse-100', 5: 'baidu-inc', 6: 'bitcoin', 7: 'bitcoin', 8: 'ftse-100', 9: 'baidu-inc', 10: 'baidu-inc', 11: 'ftse-100', 12: 'bitcoin', 13: 'baidu-inc', 14: 'ftse-100', 15: 'bitcoin', 16: 'ftse-100', 17: 'baidu-inc', 18: 'baidu-inc', 19: 'bitcoin'}, 'Class': {0: 'S', 1: 'E', 2: 'E', 3: 'G', 4: 'G', 5: 'S', 6: 'E', 7: 'S', 8: 'G', 9: 'G', 10: 'G', 11: 'S', 12: 'S', 13: 'S', 14: 'S', 15: 'S', 16: 'S', 17: 'E', 18: 'E', 19: 'S'}, 'Score': {0: -0.8674, 1: 0.395, 2: 0.0, 3: -0.3612, 4: 0.5023, 5: 0.0129, 6: -0.5023, 7: 0.0, 8: -0.5023, 9: 0.0, 10: -0.7579, 11: -0.8843, 12: 0.8968, 13: 0.7579, 14: 0.5466, 15: 0.2023, 16: 0.0, 17: 0.6457, 18: 0.0, 19: -0.5023}}
8
9df = pd.DataFrame.from_dict(data_as_dict)
10df['Date'] = pd.to_datetime(df['Date'])
11
12 Date Asset Class Score
130 2015-01-01 00:00:00 baidu-inc S -0.8674
141 2015-01-01 00:00:02 bitcoin E 0.3950
152 2015-01-01 00:00:02 bitcoin E 0.0000
163 2015-01-01 00:00:02 ftse-100 G -0.3612
174 2015-01-01 00:00:02 ftse-100 G 0.5023
185 2015-01-01 00:00:03 baidu-inc S 0.0129
196 2015-01-01 00:00:03 bitcoin E -0.5023
207 2015-01-01 00:00:04 bitcoin S 0.0000
218 2015-01-01 00:00:04 ftse-100 G -0.5023
229 2015-01-01 00:00:04 baidu-inc G 0.0000
2310 2015-01-01 00:00:05 baidu-inc G -0.7579
2411 2015-01-01 00:00:05 ftse-100 S -0.8843
2512 2015-01-01 00:00:05 bitcoin S 0.8968
2613 2015-01-01 00:00:06 baidu-inc S 0.7579
2714 2015-01-01 00:00:07 ftse-100 S 0.5466
2815 2015-01-01 00:00:07 bitcoin S 0.2023
2916 2015-01-01 00:00:07 ftse-100 S 0.0000
3017 2015-01-01 00:00:08 baidu-inc E 0.6457
3118 2015-01-01 00:00:08 baidu-inc E 0.0000
3219 2015-01-01 00:00:08 bitcoin S -0.5023
33
34df.groupby(['Asset','Class']).set_index('Date').resample('1h').mean()
35
36df["Date"] = df["Date"].dt.round("1H")
37cross = df.pivot_table(index="Date", columns=["Asset", "Class"], values="Score", aggfunc="mean")
38Asset baidu-inc bitcoin ftse-100
39Class E G S E S G S
40Date
412015-01-01 0.32285 -0.37895 -0.0322 -0.035767 0.1492 -0.1204 -0.112567
42If you wish to keep your original format, just melt it:
1import pandas as pd
2
3
4data_as_dict = {'Date': {0: '2015-01-01 00:00:00', 1: '2015-01-01 00:00:02', 2: '2015-01-01 00:00:02', 3: '2015-01-01 00:00:02', 4: '2015-01-01 00:00:02',
5 5: '2015-01-01 00:00:03', 6: '2015-01-01 00:00:03', 7: '2015-01-01 00:00:04', 8: '2015-01-01 00:00:04', 9: '2015-01-01 00:00:04', 10: '2015-01-01 00:00:05',
6 11: '2015-01-01 00:00:05', 12: '2015-01-01 00:00:05', 13: '2015-01-01 00:00:06', 14: '2015-01-01 00:00:07', 15: '2015-01-01 00:00:07',
7 16: '2015-01-01 00:00:07', 17: '2015-01-01 00:00:08', 18: '2015-01-01 00:00:08', 19: '2015-01-01 00:00:08'}, 'Asset': {0: 'baidu-inc', 1: 'bitcoin', 2: 'bitcoin', 3: 'ftse-100', 4: 'ftse-100', 5: 'baidu-inc', 6: 'bitcoin', 7: 'bitcoin', 8: 'ftse-100', 9: 'baidu-inc', 10: 'baidu-inc', 11: 'ftse-100', 12: 'bitcoin', 13: 'baidu-inc', 14: 'ftse-100', 15: 'bitcoin', 16: 'ftse-100', 17: 'baidu-inc', 18: 'baidu-inc', 19: 'bitcoin'}, 'Class': {0: 'S', 1: 'E', 2: 'E', 3: 'G', 4: 'G', 5: 'S', 6: 'E', 7: 'S', 8: 'G', 9: 'G', 10: 'G', 11: 'S', 12: 'S', 13: 'S', 14: 'S', 15: 'S', 16: 'S', 17: 'E', 18: 'E', 19: 'S'}, 'Score': {0: -0.8674, 1: 0.395, 2: 0.0, 3: -0.3612, 4: 0.5023, 5: 0.0129, 6: -0.5023, 7: 0.0, 8: -0.5023, 9: 0.0, 10: -0.7579, 11: -0.8843, 12: 0.8968, 13: 0.7579, 14: 0.5466, 15: 0.2023, 16: 0.0, 17: 0.6457, 18: 0.0, 19: -0.5023}}
8
9df = pd.DataFrame.from_dict(data_as_dict)
10df['Date'] = pd.to_datetime(df['Date'])
11
12 Date Asset Class Score
130 2015-01-01 00:00:00 baidu-inc S -0.8674
141 2015-01-01 00:00:02 bitcoin E 0.3950
152 2015-01-01 00:00:02 bitcoin E 0.0000
163 2015-01-01 00:00:02 ftse-100 G -0.3612
174 2015-01-01 00:00:02 ftse-100 G 0.5023
185 2015-01-01 00:00:03 baidu-inc S 0.0129
196 2015-01-01 00:00:03 bitcoin E -0.5023
207 2015-01-01 00:00:04 bitcoin S 0.0000
218 2015-01-01 00:00:04 ftse-100 G -0.5023
229 2015-01-01 00:00:04 baidu-inc G 0.0000
2310 2015-01-01 00:00:05 baidu-inc G -0.7579
2411 2015-01-01 00:00:05 ftse-100 S -0.8843
2512 2015-01-01 00:00:05 bitcoin S 0.8968
2613 2015-01-01 00:00:06 baidu-inc S 0.7579
2714 2015-01-01 00:00:07 ftse-100 S 0.5466
2815 2015-01-01 00:00:07 bitcoin S 0.2023
2916 2015-01-01 00:00:07 ftse-100 S 0.0000
3017 2015-01-01 00:00:08 baidu-inc E 0.6457
3118 2015-01-01 00:00:08 baidu-inc E 0.0000
3219 2015-01-01 00:00:08 bitcoin S -0.5023
33
34df.groupby(['Asset','Class']).set_index('Date').resample('1h').mean()
35
36df["Date"] = df["Date"].dt.round("1H")
37cross = df.pivot_table(index="Date", columns=["Asset", "Class"], values="Score", aggfunc="mean")
38Asset baidu-inc bitcoin ftse-100
39Class E G S E S G S
40Date
412015-01-01 0.32285 -0.37895 -0.0322 -0.035767 0.1492 -0.1204 -0.112567
42agg = cross.melt(ignore_index=False).reset_index()
43It returns:
1import pandas as pd
2
3
4data_as_dict = {'Date': {0: '2015-01-01 00:00:00', 1: '2015-01-01 00:00:02', 2: '2015-01-01 00:00:02', 3: '2015-01-01 00:00:02', 4: '2015-01-01 00:00:02',
5 5: '2015-01-01 00:00:03', 6: '2015-01-01 00:00:03', 7: '2015-01-01 00:00:04', 8: '2015-01-01 00:00:04', 9: '2015-01-01 00:00:04', 10: '2015-01-01 00:00:05',
6 11: '2015-01-01 00:00:05', 12: '2015-01-01 00:00:05', 13: '2015-01-01 00:00:06', 14: '2015-01-01 00:00:07', 15: '2015-01-01 00:00:07',
7 16: '2015-01-01 00:00:07', 17: '2015-01-01 00:00:08', 18: '2015-01-01 00:00:08', 19: '2015-01-01 00:00:08'}, 'Asset': {0: 'baidu-inc', 1: 'bitcoin', 2: 'bitcoin', 3: 'ftse-100', 4: 'ftse-100', 5: 'baidu-inc', 6: 'bitcoin', 7: 'bitcoin', 8: 'ftse-100', 9: 'baidu-inc', 10: 'baidu-inc', 11: 'ftse-100', 12: 'bitcoin', 13: 'baidu-inc', 14: 'ftse-100', 15: 'bitcoin', 16: 'ftse-100', 17: 'baidu-inc', 18: 'baidu-inc', 19: 'bitcoin'}, 'Class': {0: 'S', 1: 'E', 2: 'E', 3: 'G', 4: 'G', 5: 'S', 6: 'E', 7: 'S', 8: 'G', 9: 'G', 10: 'G', 11: 'S', 12: 'S', 13: 'S', 14: 'S', 15: 'S', 16: 'S', 17: 'E', 18: 'E', 19: 'S'}, 'Score': {0: -0.8674, 1: 0.395, 2: 0.0, 3: -0.3612, 4: 0.5023, 5: 0.0129, 6: -0.5023, 7: 0.0, 8: -0.5023, 9: 0.0, 10: -0.7579, 11: -0.8843, 12: 0.8968, 13: 0.7579, 14: 0.5466, 15: 0.2023, 16: 0.0, 17: 0.6457, 18: 0.0, 19: -0.5023}}
8
9df = pd.DataFrame.from_dict(data_as_dict)
10df['Date'] = pd.to_datetime(df['Date'])
11
12 Date Asset Class Score
130 2015-01-01 00:00:00 baidu-inc S -0.8674
141 2015-01-01 00:00:02 bitcoin E 0.3950
152 2015-01-01 00:00:02 bitcoin E 0.0000
163 2015-01-01 00:00:02 ftse-100 G -0.3612
174 2015-01-01 00:00:02 ftse-100 G 0.5023
185 2015-01-01 00:00:03 baidu-inc S 0.0129
196 2015-01-01 00:00:03 bitcoin E -0.5023
207 2015-01-01 00:00:04 bitcoin S 0.0000
218 2015-01-01 00:00:04 ftse-100 G -0.5023
229 2015-01-01 00:00:04 baidu-inc G 0.0000
2310 2015-01-01 00:00:05 baidu-inc G -0.7579
2411 2015-01-01 00:00:05 ftse-100 S -0.8843
2512 2015-01-01 00:00:05 bitcoin S 0.8968
2613 2015-01-01 00:00:06 baidu-inc S 0.7579
2714 2015-01-01 00:00:07 ftse-100 S 0.5466
2815 2015-01-01 00:00:07 bitcoin S 0.2023
2916 2015-01-01 00:00:07 ftse-100 S 0.0000
3017 2015-01-01 00:00:08 baidu-inc E 0.6457
3118 2015-01-01 00:00:08 baidu-inc E 0.0000
3219 2015-01-01 00:00:08 bitcoin S -0.5023
33
34df.groupby(['Asset','Class']).set_index('Date').resample('1h').mean()
35
36df["Date"] = df["Date"].dt.round("1H")
37cross = df.pivot_table(index="Date", columns=["Asset", "Class"], values="Score", aggfunc="mean")
38Asset baidu-inc bitcoin ftse-100
39Class E G S E S G S
40Date
412015-01-01 0.32285 -0.37895 -0.0322 -0.035767 0.1492 -0.1204 -0.112567
42agg = cross.melt(ignore_index=False).reset_index()
43 Date Asset Class value
440 2015-01-01 baidu-inc E 0.322850
451 2015-01-01 baidu-inc G -0.378950
462 2015-01-01 baidu-inc S -0.032200
473 2015-01-01 bitcoin E -0.035767
484 2015-01-01 bitcoin S 0.149200
495 2015-01-01 ftse-100 G -0.120400
506 2015-01-01 ftse-100 S -0.112567
51If you wish to keep your format, another solution is:
1import pandas as pd
2
3
4data_as_dict = {'Date': {0: '2015-01-01 00:00:00', 1: '2015-01-01 00:00:02', 2: '2015-01-01 00:00:02', 3: '2015-01-01 00:00:02', 4: '2015-01-01 00:00:02',
5 5: '2015-01-01 00:00:03', 6: '2015-01-01 00:00:03', 7: '2015-01-01 00:00:04', 8: '2015-01-01 00:00:04', 9: '2015-01-01 00:00:04', 10: '2015-01-01 00:00:05',
6 11: '2015-01-01 00:00:05', 12: '2015-01-01 00:00:05', 13: '2015-01-01 00:00:06', 14: '2015-01-01 00:00:07', 15: '2015-01-01 00:00:07',
7 16: '2015-01-01 00:00:07', 17: '2015-01-01 00:00:08', 18: '2015-01-01 00:00:08', 19: '2015-01-01 00:00:08'}, 'Asset': {0: 'baidu-inc', 1: 'bitcoin', 2: 'bitcoin', 3: 'ftse-100', 4: 'ftse-100', 5: 'baidu-inc', 6: 'bitcoin', 7: 'bitcoin', 8: 'ftse-100', 9: 'baidu-inc', 10: 'baidu-inc', 11: 'ftse-100', 12: 'bitcoin', 13: 'baidu-inc', 14: 'ftse-100', 15: 'bitcoin', 16: 'ftse-100', 17: 'baidu-inc', 18: 'baidu-inc', 19: 'bitcoin'}, 'Class': {0: 'S', 1: 'E', 2: 'E', 3: 'G', 4: 'G', 5: 'S', 6: 'E', 7: 'S', 8: 'G', 9: 'G', 10: 'G', 11: 'S', 12: 'S', 13: 'S', 14: 'S', 15: 'S', 16: 'S', 17: 'E', 18: 'E', 19: 'S'}, 'Score': {0: -0.8674, 1: 0.395, 2: 0.0, 3: -0.3612, 4: 0.5023, 5: 0.0129, 6: -0.5023, 7: 0.0, 8: -0.5023, 9: 0.0, 10: -0.7579, 11: -0.8843, 12: 0.8968, 13: 0.7579, 14: 0.5466, 15: 0.2023, 16: 0.0, 17: 0.6457, 18: 0.0, 19: -0.5023}}
8
9df = pd.DataFrame.from_dict(data_as_dict)
10df['Date'] = pd.to_datetime(df['Date'])
11
12 Date Asset Class Score
130 2015-01-01 00:00:00 baidu-inc S -0.8674
141 2015-01-01 00:00:02 bitcoin E 0.3950
152 2015-01-01 00:00:02 bitcoin E 0.0000
163 2015-01-01 00:00:02 ftse-100 G -0.3612
174 2015-01-01 00:00:02 ftse-100 G 0.5023
185 2015-01-01 00:00:03 baidu-inc S 0.0129
196 2015-01-01 00:00:03 bitcoin E -0.5023
207 2015-01-01 00:00:04 bitcoin S 0.0000
218 2015-01-01 00:00:04 ftse-100 G -0.5023
229 2015-01-01 00:00:04 baidu-inc G 0.0000
2310 2015-01-01 00:00:05 baidu-inc G -0.7579
2411 2015-01-01 00:00:05 ftse-100 S -0.8843
2512 2015-01-01 00:00:05 bitcoin S 0.8968
2613 2015-01-01 00:00:06 baidu-inc S 0.7579
2714 2015-01-01 00:00:07 ftse-100 S 0.5466
2815 2015-01-01 00:00:07 bitcoin S 0.2023
2916 2015-01-01 00:00:07 ftse-100 S 0.0000
3017 2015-01-01 00:00:08 baidu-inc E 0.6457
3118 2015-01-01 00:00:08 baidu-inc E 0.0000
3219 2015-01-01 00:00:08 bitcoin S -0.5023
33
34df.groupby(['Asset','Class']).set_index('Date').resample('1h').mean()
35
36df["Date"] = df["Date"].dt.round("1H")
37cross = df.pivot_table(index="Date", columns=["Asset", "Class"], values="Score", aggfunc="mean")
38Asset baidu-inc bitcoin ftse-100
39Class E G S E S G S
40Date
412015-01-01 0.32285 -0.37895 -0.0322 -0.035767 0.1492 -0.1204 -0.112567
42agg = cross.melt(ignore_index=False).reset_index()
43 Date Asset Class value
440 2015-01-01 baidu-inc E 0.322850
451 2015-01-01 baidu-inc G -0.378950
462 2015-01-01 baidu-inc S -0.032200
473 2015-01-01 bitcoin E -0.035767
484 2015-01-01 bitcoin S 0.149200
495 2015-01-01 ftse-100 G -0.120400
506 2015-01-01 ftse-100 S -0.112567
51df["Date"] = df["Date"].dt.round("1H")
52agg = df.groupby(["Date", "Asset", "Class"])["Score"].mean().reset_index()
53In this case, it is more straight forward.
QUESTION
How to fix LevelDB library load error when running RSKj node on a Windows machine?
Asked 2022-Jan-06 at 09:47I am trying to run RSK blockchain node RSKj on a Windows machine. When I run this line in a terminal:
1C:\Users\yemode> java -cp C:\Users\yemode\Downloads\Programs\rskj-core-3.0.1-IRIS-all.jar co.rsk.Start
2the RSKj node starts running, but I get the following error:
1C:\Users\yemode> java -cp C:\Users\yemode\Downloads\Programs\rskj-core-3.0.1-IRIS-all.jar co.rsk.Start
2Cannot load secp256k1 native library: java.lang.Exception: No native library is found for os.name=Windows and os.arch=x86. path=/org/bitcoin/native/Windows/x86
3Exception in thread "main" java.lang.RuntimeException: Can't initialize database
4 at org.ethereum.datasource.LevelDbDataSource.init(LevelDbDataSource.java:110)
5 at org.ethereum.datasource.LevelDbDataSource.makeDataSource(LevelDbDataSource.java:70)
6 at co.rsk.RskContext.buildTrieStore(RskContext.java:1015)
7 at co.rsk.RskContext.buildAbstractTrieStore(RskContext.java:935)
8 at co.rsk.RskContext.getTrieStore(RskContext.java:416)
9 at co.rsk.RskContext.buildRepositoryLocator(RskContext.java:1057)
10 at co.rsk.RskContext.getRepositoryLocator(RskContext.java:384)
11 at co.rsk.RskContext.getTransactionPool(RskContext.java:353)
12 at co.rsk.RskContext.buildInternalServices(RskContext.java:829)
13 at co.rsk.RskContext.buildNodeRunner(RskContext.java:821)
14 at co.rsk.RskContext.getNodeRunner(RskContext.java:302)
15 at co.rsk.Start.main(Start.java:34)
16What could be the problem here?
ANSWER
Answered 2021-Oct-06 at 02:26This is actually a warning, not an error, though it may seem like the latter. This means that on your OS and architecture, that particular library does not exist, so it falls back to a different implementation (using a non-native library). In this case, the block verification is slower, but otherwise RSKj should continue to function properly.
Something that might help you to overcome the “slowness” of the initial sync is the --import flag. See the reference in the CLI docs for RSKj.
Also you can send an RPC to ensure that your node is running OK. Run the following curl command in your terminal
1C:\Users\yemode> java -cp C:\Users\yemode\Downloads\Programs\rskj-core-3.0.1-IRIS-all.jar co.rsk.Start
2Cannot load secp256k1 native library: java.lang.Exception: No native library is found for os.name=Windows and os.arch=x86. path=/org/bitcoin/native/Windows/x86
3Exception in thread "main" java.lang.RuntimeException: Can't initialize database
4 at org.ethereum.datasource.LevelDbDataSource.init(LevelDbDataSource.java:110)
5 at org.ethereum.datasource.LevelDbDataSource.makeDataSource(LevelDbDataSource.java:70)
6 at co.rsk.RskContext.buildTrieStore(RskContext.java:1015)
7 at co.rsk.RskContext.buildAbstractTrieStore(RskContext.java:935)
8 at co.rsk.RskContext.getTrieStore(RskContext.java:416)
9 at co.rsk.RskContext.buildRepositoryLocator(RskContext.java:1057)
10 at co.rsk.RskContext.getRepositoryLocator(RskContext.java:384)
11 at co.rsk.RskContext.getTransactionPool(RskContext.java:353)
12 at co.rsk.RskContext.buildInternalServices(RskContext.java:829)
13 at co.rsk.RskContext.buildNodeRunner(RskContext.java:821)
14 at co.rsk.RskContext.getNodeRunner(RskContext.java:302)
15 at co.rsk.Start.main(Start.java:34)
16curl \
17 -X POST \
18 -H “Content-Type:application/json” \
19 --data ‘{“jsonrpc”:“2.0",“method”:“eth_blockNumber”,“params”:[],“id”:67}’ \
20 http://localhost:4444
21
22The response should be similar to this one
1C:\Users\yemode> java -cp C:\Users\yemode\Downloads\Programs\rskj-core-3.0.1-IRIS-all.jar co.rsk.Start
2Cannot load secp256k1 native library: java.lang.Exception: No native library is found for os.name=Windows and os.arch=x86. path=/org/bitcoin/native/Windows/x86
3Exception in thread "main" java.lang.RuntimeException: Can't initialize database
4 at org.ethereum.datasource.LevelDbDataSource.init(LevelDbDataSource.java:110)
5 at org.ethereum.datasource.LevelDbDataSource.makeDataSource(LevelDbDataSource.java:70)
6 at co.rsk.RskContext.buildTrieStore(RskContext.java:1015)
7 at co.rsk.RskContext.buildAbstractTrieStore(RskContext.java:935)
8 at co.rsk.RskContext.getTrieStore(RskContext.java:416)
9 at co.rsk.RskContext.buildRepositoryLocator(RskContext.java:1057)
10 at co.rsk.RskContext.getRepositoryLocator(RskContext.java:384)
11 at co.rsk.RskContext.getTransactionPool(RskContext.java:353)
12 at co.rsk.RskContext.buildInternalServices(RskContext.java:829)
13 at co.rsk.RskContext.buildNodeRunner(RskContext.java:821)
14 at co.rsk.RskContext.getNodeRunner(RskContext.java:302)
15 at co.rsk.Start.main(Start.java:34)
16curl \
17 -X POST \
18 -H “Content-Type:application/json” \
19 --data ‘{“jsonrpc”:“2.0",“method”:“eth_blockNumber”,“params”:[],“id”:67}’ \
20 http://localhost:4444
21
22{“jsonrpc”:“2.0",“id”:67,“result”:“0x2b12”}
23where the result is your last block number
QUESTION
after updating flutter, Firebase.initializeApp() does not seem to work on my phone anymore
Asked 2022-Jan-04 at 09:01I updated the flutter to the latest version today and after that the app is not loading on real device because something is going wrong with Firebase.initializeApp().
It does seem to still work on the android emulator but not on real device.
This is the main.dart:
1import 'dart:io';
2
3import 'package:flutter/material.dart';
4import 'package:firebase_core/firebase_core.dart';
5import 'package:major_capital_mobile_app/pages/login_page.dart';
6
7void main() {
8 HttpOverrides.global = MyHttpOverrides();
9 WidgetsFlutterBinding.ensureInitialized();
10 runApp(const App());
11}
12
13class App extends StatefulWidget {
14 const App({Key? key}) : super(key: key);
15
16 // Create the initialization Future outside of `build`:
17 @override
18 _AppState createState() => _AppState();
19}
20
21class MyHttpOverrides extends HttpOverrides {
22 @override
23 HttpClient createHttpClient(SecurityContext? context) {
24 return super.createHttpClient(context)
25 ..badCertificateCallback =
26 (X509Certificate cert, String host, int port) => true;
27 }
28}
29
30class _AppState extends State<App> {
31 final Future<FirebaseApp> _initialization = Firebase.initializeApp();
32
33 @override
34 Widget build(BuildContext context) {
35 return FutureBuilder(
36 // Initialize FlutterFire:
37 future: _initialization,
38 builder: (context, snapshot) {
39 // Check for errors
40 if (snapshot.hasError) {
41 return const Center(
42 child:
43 Text('Not able to load app', textDirection: TextDirection.ltr),
44 );
45 }
46
47 // Show app once complete
48 if (snapshot.connectionState == ConnectionState.done) {
49 return MaterialApp(
50 title: 'Verification of phone',
51 debugShowCheckedModeBanner: false,
52 theme: ThemeData(
53 primaryColor: Colors.black,
54 primarySwatch: Colors.blue,
55 inputDecorationTheme: const InputDecorationTheme(
56 labelStyle: TextStyle(color: Colors.grey)),
57 backgroundColor: Colors.black),
58 home: const LoginPage(),
59 );
60 }
61
62 // Else wait until initialization
63 return Column(
64 crossAxisAlignment: CrossAxisAlignment.start,
65 children: <Widget>[
66 Row(
67 mainAxisAlignment: MainAxisAlignment.center,
68 crossAxisAlignment: CrossAxisAlignment.center,
69 children: <Widget>[
70 CircularProgressIndicator(
71 color: Colors.lime,
72 backgroundColor: Theme.of(context).primaryColor,
73 )
74 ])
75 ]);
76 },
77 );
78 }
79}
80
81And this is the pubspec.yaml file:
1import 'dart:io';
2
3import 'package:flutter/material.dart';
4import 'package:firebase_core/firebase_core.dart';
5import 'package:major_capital_mobile_app/pages/login_page.dart';
6
7void main() {
8 HttpOverrides.global = MyHttpOverrides();
9 WidgetsFlutterBinding.ensureInitialized();
10 runApp(const App());
11}
12
13class App extends StatefulWidget {
14 const App({Key? key}) : super(key: key);
15
16 // Create the initialization Future outside of `build`:
17 @override
18 _AppState createState() => _AppState();
19}
20
21class MyHttpOverrides extends HttpOverrides {
22 @override
23 HttpClient createHttpClient(SecurityContext? context) {
24 return super.createHttpClient(context)
25 ..badCertificateCallback =
26 (X509Certificate cert, String host, int port) => true;
27 }
28}
29
30class _AppState extends State<App> {
31 final Future<FirebaseApp> _initialization = Firebase.initializeApp();
32
33 @override
34 Widget build(BuildContext context) {
35 return FutureBuilder(
36 // Initialize FlutterFire:
37 future: _initialization,
38 builder: (context, snapshot) {
39 // Check for errors
40 if (snapshot.hasError) {
41 return const Center(
42 child:
43 Text('Not able to load app', textDirection: TextDirection.ltr),
44 );
45 }
46
47 // Show app once complete
48 if (snapshot.connectionState == ConnectionState.done) {
49 return MaterialApp(
50 title: 'Verification of phone',
51 debugShowCheckedModeBanner: false,
52 theme: ThemeData(
53 primaryColor: Colors.black,
54 primarySwatch: Colors.blue,
55 inputDecorationTheme: const InputDecorationTheme(
56 labelStyle: TextStyle(color: Colors.grey)),
57 backgroundColor: Colors.black),
58 home: const LoginPage(),
59 );
60 }
61
62 // Else wait until initialization
63 return Column(
64 crossAxisAlignment: CrossAxisAlignment.start,
65 children: <Widget>[
66 Row(
67 mainAxisAlignment: MainAxisAlignment.center,
68 crossAxisAlignment: CrossAxisAlignment.center,
69 children: <Widget>[
70 CircularProgressIndicator(
71 color: Colors.lime,
72 backgroundColor: Theme.of(context).primaryColor,
73 )
74 ])
75 ]);
76 },
77 );
78 }
79}
80
81name: major_capital_mobile_app
82description: A new Flutter project.
83
84# The following line prevents the package from being accidentally published to
85# pub.dev using `flutter pub publish`. This is preferred for private packages.
86publish_to: 'none' # Remove this line if you wish to publish to pub.dev
87
88# The following defines the version and build number for your application.
89# A version number is three numbers separated by dots, like 1.2.43
90# followed by an optional build number separated by a +.
91# Both the version and the builder number may be overridden in flutter
92# build by specifying --build-name and --build-number, respectively.
93# In Android, build-name is used as versionName while build-number used as versionCode.
94# Read more about Android versioning at https://developer.android.com/studio/publish/versioning
95# In iOS, build-name is used as CFBundleShortVersionString while build-number used as CFBundleVersion.
96# Read more about iOS versioning at
97# https://developer.apple.com/library/archive/documentation/General/Reference/InfoPlistKeyReference/Articles/CoreFoundationKeys.html
98version: 1.0.0+1
99
100environment:
101 sdk: ">=2.14.0 <3.0.0"
102
103# Dependencies specify other packages that your package needs in order to work.
104# To automatically upgrade your package dependencies to the latest versions
105# consider running `flutter pub upgrade --major-versions`. Alternatively,
106# dependencies can be manually updated by changing the version numbers below to
107# the latest version available on pub.dev. To see which dependencies have newer
108# versions available, run `flutter pub outdated`.
109dependencies:
110 flutter:
111 sdk: flutter
112
113
114
115 # The following adds the Cupertino Icons font to your application.
116 # Use with the CupertinoIcons class for iOS style icons.
117
118 cupertino_icons: ^1.0.2
119 firebase_core: ^1.7.0
120 firebase_auth: ^3.1.2
121 cloud_firestore: ^2.5.3
122 email_auth: ^1.0.0
123 hexcolor: ^2.0.5
124 fluttertoast: ^8.0.8
125 cloud_functions: ^3.0.4
126 device_info: ^2.0.2
127 package_info: ^2.0.2
128 provider: ^6.0.1
129 get: ^4.3.8
130 syncfusion_flutter_charts: ^19.3.48
131 google_fonts: ^2.1.0
132 expandable: ^5.0.1
133 charts_flutter: ^0.12.0
134 font_awesome_flutter: ^9.2.0
135 chartjs: ^0.6.0
136 fl_chart: ^0.40.2
137 desktop_window: ^0.4.0
138 syncfusion_flutter_datagrid: ^19.3.53
139 http: ^0.13.4
140 shared_preferences: ^2.0.8
141 mockito: ^5.0.16
142 rxdart: ^0.27.3
143 pinput: ^1.2.2
144 auto_size_text: ^3.0.0
145
146
147dev_dependencies:
148 flutter_test:
149 sdk: flutter
150
151
152 # The "flutter_lints" package below contains a set of recommended lints to
153 # encourage good coding practices. The lint set provided by the package is
154 # activated in the `analysis_options.yaml` file located at the root of your
155 # package. See that file for information about deactivating specific lint
156 # rules and activating additional ones.
157 flutter_lints: ^1.0.0
158
159# For information on the generic Dart part of this file, see the
160# following page: https://dart.dev/tools/pub/pubspec
161
162# The following section is specific to Flutter.
163flutter:
164 assets:
165 - assets/data.json
166 - assets/rounded.png
167 - assets/mcm_white_logo.png
168 - treeline2_Tekengebied.png
169 - b_a_token.png
170 - binance_coin.png
171 - bitcoin.png
172 - ethereum.png
173
174 # The following line ensures that the Material Icons font is
175 # included with your application, so that you can use the icons in
176 # the material Icons class.
177 uses-material-design: true
178
179
180 # To add assets to your application, add an assets section, like this:
181 # assets:
182 # - images/a_dot_burr.jpeg
183 # - images/a_dot_ham.jpeg
184
185 # An image asset can refer to one or more resolution-specific "variants", see
186 # https://flutter.dev/assets-and-images/#resolution-aware.
187
188 # For details regarding adding assets from package dependencies, see
189 # https://flutter.dev/assets-and-images/#from-packages
190
191 # To add custom fonts to your application, add a fonts section here,
192 # in this "flutter" section. Each entry in this list should have a
193 # "family" key with the font family name, and a "fonts" key with a
194 # list giving the asset and other descriptors for the font. For
195 # example:
196 fonts:
197 - family: CustomIcons
198 fonts:
199 - asset: lib/icons/fonts/CustomIcons.ttf
200
201 #
202 # For details regarding fonts from package dependencies,
203 # see https://flutter.dev/custom-fonts/#from-packages
204
205Like i said it seems to work on android emulator but once i try it on real device i get the message not able to load app on the screen so it seems it can load the app but its not successfully doing initiliazation of Firebase.initializeApp(). to show the whole app.
ANSWER
Answered 2022-Jan-03 at 14:05Do you give permission for internet in Manifest file of an android?
QUESTION
How to retreive the date when video has been released on Youtube using Python and Selenium
Asked 2022-Jan-03 at 15:57I made a script in python and selenium that makes a search on youtube. When it's completely loaded, I'm only able to fetch all titles from the results. Is there any line of code I can integrate in order to fetch date publishing too?
This is my code:
1def youTube():
2 term = 'bitcoin'
3 tit = []
4
5 d = webdriver.Firefox()
6 d.get('https://www.youtube.com/results?search_query='+term+'&sp=CAISAhAB')
7 sleep(3)
8
9 d.find_element_by_xpath("//*[contains(text(), 'Accetto')]").click()
10 sleep(2)
11
12 scrollHeight = d.execute_script("return window.scrollMaxY")
13 print(scrollHeight)
14 scrolled_pages = 0
15 # while we have not reached the max scrollHeight
16 while d.execute_script("return window.pageYOffset") < 3000:
17 d.execute_script("window.scrollByPages(1)")
18 scrolled_pages += 1
19 sleep(0.2)
20 for my_elem in WebDriverWait(d, 20).until(EC.visibility_of_all_elements_located((By.XPATH, "//yt-formatted-string[@class='style-scope ytd-video-renderer' and @aria-label]"))):
21 tit.append(my_elem.text)
22ANSWER
Answered 2022-Jan-03 at 15:57To retrieve the date/time when video has been released on Youtube using Python and Selenium you can use the following Locator Strategy:
Code Block:
1def youTube():
2 term = 'bitcoin'
3 tit = []
4
5 d = webdriver.Firefox()
6 d.get('https://www.youtube.com/results?search_query='+term+'&sp=CAISAhAB')
7 sleep(3)
8
9 d.find_element_by_xpath("//*[contains(text(), 'Accetto')]").click()
10 sleep(2)
11
12 scrollHeight = d.execute_script("return window.scrollMaxY")
13 print(scrollHeight)
14 scrolled_pages = 0
15 # while we have not reached the max scrollHeight
16 while d.execute_script("return window.pageYOffset") < 3000:
17 d.execute_script("window.scrollByPages(1)")
18 scrolled_pages += 1
19 sleep(0.2)
20 for my_elem in WebDriverWait(d, 20).until(EC.visibility_of_all_elements_located((By.XPATH, "//yt-formatted-string[@class='style-scope ytd-video-renderer' and @aria-label]"))):
21 tit.append(my_elem.text)
22driver.get("https://www.youtube.com/results?search_query=%27+term+%27&sp=CAISAhAB")
23print([my_elem.get_attribute("innerHTML") for my_elem in WebDriverWait(driver, 20).until(EC.visibility_of_all_elements_located((By.XPATH, "//div[@id='metadata-line' and @class='style-scope ytd-video-meta-block']////following::span[2][contains(., 'hours') or contains(., 'day')]")))])
24Console Output:
1def youTube():
2 term = 'bitcoin'
3 tit = []
4
5 d = webdriver.Firefox()
6 d.get('https://www.youtube.com/results?search_query='+term+'&sp=CAISAhAB')
7 sleep(3)
8
9 d.find_element_by_xpath("//*[contains(text(), 'Accetto')]").click()
10 sleep(2)
11
12 scrollHeight = d.execute_script("return window.scrollMaxY")
13 print(scrollHeight)
14 scrolled_pages = 0
15 # while we have not reached the max scrollHeight
16 while d.execute_script("return window.pageYOffset") < 3000:
17 d.execute_script("window.scrollByPages(1)")
18 scrolled_pages += 1
19 sleep(0.2)
20 for my_elem in WebDriverWait(d, 20).until(EC.visibility_of_all_elements_located((By.XPATH, "//yt-formatted-string[@class='style-scope ytd-video-renderer' and @aria-label]"))):
21 tit.append(my_elem.text)
22driver.get("https://www.youtube.com/results?search_query=%27+term+%27&sp=CAISAhAB")
23print([my_elem.get_attribute("innerHTML") for my_elem in WebDriverWait(driver, 20).until(EC.visibility_of_all_elements_located((By.XPATH, "//div[@id='metadata-line' and @class='style-scope ytd-video-meta-block']////following::span[2][contains(., 'hours') or contains(., 'day')]")))])
24['2 hours ago', '2 hours ago', '3 hours ago', 'Streamed 3 hours ago', 'Streamed 3 hours ago', '5 hours ago', 'Streamed 6 hours ago', '6 hours ago', '6 hours ago', '7 hours ago', '8 hours ago', 'Streamed 8 hours ago', '9 hours ago', 'Streamed 10 hours ago', '11 hours ago']
25QUESTION
Calculating Bitcoin mining difficulty
Asked 2021-Dec-24 at 09:38I am working on a project that requires me to get real time mining difficulty of bitcoin. So I read on this page that explains how to get the mining difficulty from an hash of a block : https://en.bitcoin.it/wiki/Difficulty
So I made this python script that collect all hashes from the Blockchain api between two dates.(https://www.blockchain.com/api) and calculate mining difficulty from the hashes.
But when I plot the result, I have something really different from every other mining difficulty that I saw online. The mining difficulty is really messy as you can see here :
x= time , y = difficulty
Here is when I apply a np.log to the difficulty:
x= time , y = np.log(difficulty)
As you can see, the result is really messy. So I wondered if there is a crypto expert that is able to tell what is wrong with my code of my formula (or maybe if I am right) :)
Here is my code :
1import requests, json
2from datetime import datetime
3import pandas as pd
4import numpy as np
5import matplotlib.pyplot as plt
6
7start = "2021-01-01"#The script start to collect hashes from this date
8end= "2021-12-01"#And end at this one
9
10timestamp_start = datetime.strptime(start, "%Y-%m-%d").timestamp()
11timestamp_end = datetime.strptime(end, "%Y-%m-%d").timestamp()
12new_start = timestamp_start
13
14datas = pd.DataFrame([], columns = ["time", "difficulty"])
15
16dec_max_diff = int("00000000FFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFFF", 16)#maximum target that is used to calculate mining difficulty
17
18while True:
19
20 if new_start > timestamp_end:
21 break
22
23
24 url = f"https://blockchain.info/blocks/{int(new_start*1000)}?format=json"
25 response = requests.get(url)#Make a request to the API
26 list_block_adress = json.loads(response.text)#Get a json containing the hash and a timestamp
27
28
29 for block_adress in list_block_adress:
30
31 dec_hash = int(block_adress["hash"], 16)
32
33 difficulty = dec_max_diff / dec_hash #Formula to calculate mining difficulty
34
35 data = pd.DataFrame([[block_adress["time"], difficulty]], columns = ["time", "difficulty"])
36
37 datas = pd.concat([datas, data])
38
39 new_start += 60*60*24 #For the loop to continue
40
41
42#Sorting and cleaning up the datas
43datas.sort_values(by='time', inplace= True)
44datas.drop_duplicates(subset='time', keep="first")
45
46#Ploting the datas
47times = pd.to_datetime(datas["time"], unit= "ms").to_numpy()
48difficulties= datas["difficulty"].apply(lambda x: np.log(x)).to_numpy()
49plt.plot(times, difficulties)
50plt.show()
51ANSWER
Answered 2021-Dec-23 at 21:10Since the hashes are effectively random, with each bit independent, there is a chance that, in addition to the n required 0s for the proof of work, some following bits are 0 as well. (These hashes would have been valid even if n had been higher at the time, but that’s not relevant.) The “dynamic range” here, relative to the floor of the noise which may be taken as the required difficulty, seems to cover about e10=22000, which is easily appropriate for the number of blocks mined.
(The actual n isn’t an integer, since the target isn’t a power of 2, but this gives the correct idea.)
QUESTION
Python - BeautifulSoup - Selecting a 'div' with 'class'-attribute shows every div in the html
Asked 2021-Dec-01 at 11:34I'm trying to crawl coinmarketcap.com with BeautifulSoup (I know there is an API, for training purposes, I want to use BeautifulSoup). Every piece of information crawled so far was pretty easy to select, but now I like to get the "Holder Statistics" looking like this:
My testing code for selecting the specific div containing the desired information looks like this:
1import requests
2from bs4 import BeautifulSoup
3
4url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
5response = requests.get(url)
6soup = BeautifulSoup(response.text, 'html.parser')
7holders = soup.select('div', class_='n0m7sa-0 kkBhMM')
8print(holders)
9The output of print(holders) is not the expected content of the div, but rather the whole html content of the website. I append a picture of this because the output code would be too long.
Does anybody know, why this is the case?
ANSWER
Answered 2021-Dec-01 at 11:34You should use .select() when you want to use as css selector. In this case, holders = soup.select('div', class_='n0m7sa-0 kkBhMM') the class part is essentially ignored...and it finds all the <div> with any class. To specify that particular class use either the .find_all(), or change your .select()
holders = soup.select('div.n0m7sa-0.kkBhMM')
or
holders = soup.find_all('div', class_='n0m7sa-0 kkBhMM')
Now in both of these cases, it will return None or an empty list. That is because that class attribute is not in the source html. This site is dynamic, so those classes are generated after the initial request. So you either need to use Selenium to render the page first, then pull the html, or see if there's an api to get the data source directly.
There is an api to get the data:
1import requests
2from bs4 import BeautifulSoup
3
4url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
5response = requests.get(url)
6soup = BeautifulSoup(response.text, 'html.parser')
7holders = soup.select('div', class_='n0m7sa-0 kkBhMM')
8print(holders)
9import requests
10import pandas as pd
11
12alpha = ['count', 'ratio']
13payload = {
14'id': '1',
15'range': '7d'}
16
17
18for each in alpha:
19 url = f'https://api.coinmarketcap.com/data-api/v3/cryptocurrency/detail/holders/{each}'
20 jsonData = requests.get(url, params=payload).json()['data']['points']
21
22 if each == 'count':
23 count_df = pd.DataFrame.from_dict(jsonData,orient='index')
24 count_df = count_df.rename(columns={0:'Total Addresses'})
25
26 else:
27 ratio_df = pd.DataFrame.from_dict(jsonData,orient='index')
28 df = count_df.merge(ratio_df, how='left', left_index=True, right_index=True)
29
30df = df.sort_index()
31
32Output:
1import requests
2from bs4 import BeautifulSoup
3
4url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
5response = requests.get(url)
6soup = BeautifulSoup(response.text, 'html.parser')
7holders = soup.select('div', class_='n0m7sa-0 kkBhMM')
8print(holders)
9import requests
10import pandas as pd
11
12alpha = ['count', 'ratio']
13payload = {
14'id': '1',
15'range': '7d'}
16
17
18for each in alpha:
19 url = f'https://api.coinmarketcap.com/data-api/v3/cryptocurrency/detail/holders/{each}'
20 jsonData = requests.get(url, params=payload).json()['data']['points']
21
22 if each == 'count':
23 count_df = pd.DataFrame.from_dict(jsonData,orient='index')
24 count_df = count_df.rename(columns={0:'Total Addresses'})
25
26 else:
27 ratio_df = pd.DataFrame.from_dict(jsonData,orient='index')
28 df = count_df.merge(ratio_df, how='left', left_index=True, right_index=True)
29
30df = df.sort_index()
31
32print(df.to_string())
33 Total Addresses topTenHolderRatio topTwentyHolderRatio topFiftyHolderRatio topHundredHolderRatio
342021-11-24T00:00:00Z 39279627 5.25 7.19 10.51 13.26
352021-11-25T00:00:00Z 39255811 5.25 7.19 10.49 13.22
362021-11-26T00:00:00Z 39339840 5.25 7.19 10.51 13.24
372021-11-27T00:00:00Z 39391849 5.23 7.11 10.45 13.18
382021-11-28T00:00:00Z 39505340 5.24 7.11 10.45 13.18
392021-11-29T00:00:00Z 39502099 5.24 7.11 10.43 13.16
402021-11-30T00:00:00Z 39523000 5.24 7.11 10.38 13.12
41Your Other option is that the data is within the <script> tags in json format. S0 you can pull it out from the initial request site that way too:
1import requests
2from bs4 import BeautifulSoup
3
4url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
5response = requests.get(url)
6soup = BeautifulSoup(response.text, 'html.parser')
7holders = soup.select('div', class_='n0m7sa-0 kkBhMM')
8print(holders)
9import requests
10import pandas as pd
11
12alpha = ['count', 'ratio']
13payload = {
14'id': '1',
15'range': '7d'}
16
17
18for each in alpha:
19 url = f'https://api.coinmarketcap.com/data-api/v3/cryptocurrency/detail/holders/{each}'
20 jsonData = requests.get(url, params=payload).json()['data']['points']
21
22 if each == 'count':
23 count_df = pd.DataFrame.from_dict(jsonData,orient='index')
24 count_df = count_df.rename(columns={0:'Total Addresses'})
25
26 else:
27 ratio_df = pd.DataFrame.from_dict(jsonData,orient='index')
28 df = count_df.merge(ratio_df, how='left', left_index=True, right_index=True)
29
30df = df.sort_index()
31
32print(df.to_string())
33 Total Addresses topTenHolderRatio topTwentyHolderRatio topFiftyHolderRatio topHundredHolderRatio
342021-11-24T00:00:00Z 39279627 5.25 7.19 10.51 13.26
352021-11-25T00:00:00Z 39255811 5.25 7.19 10.49 13.22
362021-11-26T00:00:00Z 39339840 5.25 7.19 10.51 13.24
372021-11-27T00:00:00Z 39391849 5.23 7.11 10.45 13.18
382021-11-28T00:00:00Z 39505340 5.24 7.11 10.45 13.18
392021-11-29T00:00:00Z 39502099 5.24 7.11 10.43 13.16
402021-11-30T00:00:00Z 39523000 5.24 7.11 10.38 13.12
41from bs4 import BeautifulSoup
42import requests
43import json
44import re
45
46url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
47response = requests.get(url)
48soup = BeautifulSoup(response.text, 'html.parser')
49
50jsonStr = str(soup.find('script', {'id':'__NEXT_DATA__'}))
51jsonStr = re.search(r"({.*})", jsonStr).groups()[0]
52jsonData = json.loads(jsonStr)['props']['initialProps']['pageProps']['info']['holders']
53
54df = pd.DataFrame(jsonData).drop('holderList', axis=1).drop_duplicates()
55Output:
1import requests
2from bs4 import BeautifulSoup
3
4url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
5response = requests.get(url)
6soup = BeautifulSoup(response.text, 'html.parser')
7holders = soup.select('div', class_='n0m7sa-0 kkBhMM')
8print(holders)
9import requests
10import pandas as pd
11
12alpha = ['count', 'ratio']
13payload = {
14'id': '1',
15'range': '7d'}
16
17
18for each in alpha:
19 url = f'https://api.coinmarketcap.com/data-api/v3/cryptocurrency/detail/holders/{each}'
20 jsonData = requests.get(url, params=payload).json()['data']['points']
21
22 if each == 'count':
23 count_df = pd.DataFrame.from_dict(jsonData,orient='index')
24 count_df = count_df.rename(columns={0:'Total Addresses'})
25
26 else:
27 ratio_df = pd.DataFrame.from_dict(jsonData,orient='index')
28 df = count_df.merge(ratio_df, how='left', left_index=True, right_index=True)
29
30df = df.sort_index()
31
32print(df.to_string())
33 Total Addresses topTenHolderRatio topTwentyHolderRatio topFiftyHolderRatio topHundredHolderRatio
342021-11-24T00:00:00Z 39279627 5.25 7.19 10.51 13.26
352021-11-25T00:00:00Z 39255811 5.25 7.19 10.49 13.22
362021-11-26T00:00:00Z 39339840 5.25 7.19 10.51 13.24
372021-11-27T00:00:00Z 39391849 5.23 7.11 10.45 13.18
382021-11-28T00:00:00Z 39505340 5.24 7.11 10.45 13.18
392021-11-29T00:00:00Z 39502099 5.24 7.11 10.43 13.16
402021-11-30T00:00:00Z 39523000 5.24 7.11 10.38 13.12
41from bs4 import BeautifulSoup
42import requests
43import json
44import re
45
46url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
47response = requests.get(url)
48soup = BeautifulSoup(response.text, 'html.parser')
49
50jsonStr = str(soup.find('script', {'id':'__NEXT_DATA__'}))
51jsonStr = re.search(r"({.*})", jsonStr).groups()[0]
52jsonData = json.loads(jsonStr)['props']['initialProps']['pageProps']['info']['holders']
53
54df = pd.DataFrame(jsonData).drop('holderList', axis=1).drop_duplicates()
55print(df.to_string())
56 holderCount dailyActive topTenHolderRatio topTwentyHolderRatio topFiftyHolderRatio topHundredHolderRatio
570 39523000 963625 5.24 7.11 10.38 13.12
58For the Social Stats in the Project Info, that's within a specific api:
1import requests
2from bs4 import BeautifulSoup
3
4url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
5response = requests.get(url)
6soup = BeautifulSoup(response.text, 'html.parser')
7holders = soup.select('div', class_='n0m7sa-0 kkBhMM')
8print(holders)
9import requests
10import pandas as pd
11
12alpha = ['count', 'ratio']
13payload = {
14'id': '1',
15'range': '7d'}
16
17
18for each in alpha:
19 url = f'https://api.coinmarketcap.com/data-api/v3/cryptocurrency/detail/holders/{each}'
20 jsonData = requests.get(url, params=payload).json()['data']['points']
21
22 if each == 'count':
23 count_df = pd.DataFrame.from_dict(jsonData,orient='index')
24 count_df = count_df.rename(columns={0:'Total Addresses'})
25
26 else:
27 ratio_df = pd.DataFrame.from_dict(jsonData,orient='index')
28 df = count_df.merge(ratio_df, how='left', left_index=True, right_index=True)
29
30df = df.sort_index()
31
32print(df.to_string())
33 Total Addresses topTenHolderRatio topTwentyHolderRatio topFiftyHolderRatio topHundredHolderRatio
342021-11-24T00:00:00Z 39279627 5.25 7.19 10.51 13.26
352021-11-25T00:00:00Z 39255811 5.25 7.19 10.49 13.22
362021-11-26T00:00:00Z 39339840 5.25 7.19 10.51 13.24
372021-11-27T00:00:00Z 39391849 5.23 7.11 10.45 13.18
382021-11-28T00:00:00Z 39505340 5.24 7.11 10.45 13.18
392021-11-29T00:00:00Z 39502099 5.24 7.11 10.43 13.16
402021-11-30T00:00:00Z 39523000 5.24 7.11 10.38 13.12
41from bs4 import BeautifulSoup
42import requests
43import json
44import re
45
46url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
47response = requests.get(url)
48soup = BeautifulSoup(response.text, 'html.parser')
49
50jsonStr = str(soup.find('script', {'id':'__NEXT_DATA__'}))
51jsonStr = re.search(r"({.*})", jsonStr).groups()[0]
52jsonData = json.loads(jsonStr)['props']['initialProps']['pageProps']['info']['holders']
53
54df = pd.DataFrame(jsonData).drop('holderList', axis=1).drop_duplicates()
55print(df.to_string())
56 holderCount dailyActive topTenHolderRatio topTwentyHolderRatio topFiftyHolderRatio topHundredHolderRatio
570 39523000 963625 5.24 7.11 10.38 13.12
58import requests
59import pandas as pd
60
61url = 'https://api.coinmarketcap.com/data-api/v3/project-info/detail?slug=bitcoin'
62jsonData = requests.get(url).json()
63socialStats = jsonData['data']['socialStats']
64
65row = {}
66for k, v in socialStats.items():
67 if type(v) == dict:
68 row.update(v)
69 else:
70 row.update({k:v})
71
72df = pd.DataFrame([row])
73Output:
1import requests
2from bs4 import BeautifulSoup
3
4url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
5response = requests.get(url)
6soup = BeautifulSoup(response.text, 'html.parser')
7holders = soup.select('div', class_='n0m7sa-0 kkBhMM')
8print(holders)
9import requests
10import pandas as pd
11
12alpha = ['count', 'ratio']
13payload = {
14'id': '1',
15'range': '7d'}
16
17
18for each in alpha:
19 url = f'https://api.coinmarketcap.com/data-api/v3/cryptocurrency/detail/holders/{each}'
20 jsonData = requests.get(url, params=payload).json()['data']['points']
21
22 if each == 'count':
23 count_df = pd.DataFrame.from_dict(jsonData,orient='index')
24 count_df = count_df.rename(columns={0:'Total Addresses'})
25
26 else:
27 ratio_df = pd.DataFrame.from_dict(jsonData,orient='index')
28 df = count_df.merge(ratio_df, how='left', left_index=True, right_index=True)
29
30df = df.sort_index()
31
32print(df.to_string())
33 Total Addresses topTenHolderRatio topTwentyHolderRatio topFiftyHolderRatio topHundredHolderRatio
342021-11-24T00:00:00Z 39279627 5.25 7.19 10.51 13.26
352021-11-25T00:00:00Z 39255811 5.25 7.19 10.49 13.22
362021-11-26T00:00:00Z 39339840 5.25 7.19 10.51 13.24
372021-11-27T00:00:00Z 39391849 5.23 7.11 10.45 13.18
382021-11-28T00:00:00Z 39505340 5.24 7.11 10.45 13.18
392021-11-29T00:00:00Z 39502099 5.24 7.11 10.43 13.16
402021-11-30T00:00:00Z 39523000 5.24 7.11 10.38 13.12
41from bs4 import BeautifulSoup
42import requests
43import json
44import re
45
46url = 'https://coinmarketcap.com/currencies/bitcoin/holders/'
47response = requests.get(url)
48soup = BeautifulSoup(response.text, 'html.parser')
49
50jsonStr = str(soup.find('script', {'id':'__NEXT_DATA__'}))
51jsonStr = re.search(r"({.*})", jsonStr).groups()[0]
52jsonData = json.loads(jsonStr)['props']['initialProps']['pageProps']['info']['holders']
53
54df = pd.DataFrame(jsonData).drop('holderList', axis=1).drop_duplicates()
55print(df.to_string())
56 holderCount dailyActive topTenHolderRatio topTwentyHolderRatio topFiftyHolderRatio topHundredHolderRatio
570 39523000 963625 5.24 7.11 10.38 13.12
58import requests
59import pandas as pd
60
61url = 'https://api.coinmarketcap.com/data-api/v3/project-info/detail?slug=bitcoin'
62jsonData = requests.get(url).json()
63socialStats = jsonData['data']['socialStats']
64
65row = {}
66for k, v in socialStats.items():
67 if type(v) == dict:
68 row.update(v)
69 else:
70 row.update({k:v})
71
72df = pd.DataFrame([row])
73print(df.to_string())
74 cryptoId commits contributors stars forks watchers lastCommitAt members updatedTime
750 1 31588 836 59687 30692 3881 2021-11-30T00:09:02.000Z 3617460 2021-11-30T16:00:02.365Z
76Community Discussions contain sources that include Stack Exchange Network
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