def catCount(): jobs = pd.read_csv( r'C:\Users\lilyk\Desktop\Capstone_project-master\output.csv') cat = jobs['category'] count = (cat.value_counts()) a = [ 'Artificial Inetlligence', 'Software Engineer', 'Deep Learning', 'Machine Learning' ] t = [] t.append(count['Artificial Intelligence']) t.append(count['Software Engineer']) t.append(count['Deep Learning']) t.append(count['Machine Learning']) fig2 = px.bar(jobs, x=a, y=t, color=a, labels={ 'y': 'Frequency', 'x': 'Category' }, title=' Frequency of Job Categories') fig2.show() #This puts it in your cloud for your Chart Studios Account #You can switch it to be your account/api_key from your account. tls.set_credentials_file(username='******', api_key='3ztc7kdqWPHtPtkhusiy') url = py.plot(fig2, filename='categoryFig', auto_open=True) return (tls.get_embed(url))
def main(): load_dotenv() vk_service_key = os.getenv('VK_SERVICE_KEY') parser = argparse.ArgumentParser( description='This script get all mentions by keyword in vk.com \ count its and visualize with plotly bar chart.' ) parser.add_argument('keyword', help='Input keyword for search') parser.add_argument( '-d', '--days_period', default= 7, type=int, help='Enter days count to search starting today' ) args = parser.parse_args() days_period = args.days_period timestamps_for_days_period = get_utc_timestamps_for_days_period( days_period, convert_to_utc_timestamp ) keyword = args.keyword max_count = 10 mentions_by_days = get_mentions_by_days( timestamps_for_days_period, vk_service_key, keyword, max_count ) plotly_api_key = os.getenv('PLOTLY_API_KEY') plotly_username = os.getenv('PLOTLY_USERNAME') chart_studio_tools.set_credentials_file( username=plotly_username, api_key=plotly_api_key ) days = [mentions_by_day['day'] for mentions_by_day in mentions_by_days] mentions_count = [mentions_by_day['count'] for mentions_by_day in mentions_by_days] mentions_by_days_bar_chart = go.Figure([go.Bar(x=days, y=mentions_count)]) chart_url = py.plot( mentions_by_days_bar_chart , filename = '{} in vk mentions chart'.format(keyword), auto_open=True ) print(chart_url)
def plotly_url(data,user, freq='D'): """ Takes list of dicts from django model.objects.values iterable and turns it into a dataframe for use with plotly plotting. :param data: list containing all values :param freq: timeperiod, default H:hour :type data: list :type freq: str """ try: user_plotly = os.environ.get('PLOTLY_USER') api = os.environ.get('PLOTLY_API') assert len(api) > 0 assert len(user_plotly) > 0 # Make dataframe and group by month df = pd.DataFrame(data) df['created_at'] = pd.to_datetime(df['created_at']) if 'user_id' in df.columns: df.rename(columns={'user_id':'user'}) df['user'] = user # Set's up series for plotting by grouping datetime df_u = df.groupby(['user','sentiment',pd.Grouper(key='created_at',freq=freq)])['sentiment'].count().reset_index(name='total') # Get rolling avgs df_u['nroll_7'] = df_u[df_u['sentiment']=='Negative'].total.rolling(7,win_type='triang').mean() df_u['proll_7'] = df_u[df_u['sentiment']=='Positive'].total.rolling(7,win_type='triang').mean() df_u['nroll_7'] = df_u['nroll_7'].fillna(method='bfill').fillna(method='ffill') df_u['proll_7'] = df_u['proll_7'].fillna(method='bfill').fillna(method='ffill') # Plotly tls.set_credentials_file(username=user_plotly,api_key=api) user_bar = px.bar(df_u, x="created_at", y="total", color="sentiment",barmode="group", category_orders=co,labels={'created_at':'Date','total':'Total'}, title=f'{user}: Tweets',hover_data=df_u.columns) # Plot rolling averages user_bar.add_trace(go.Scatter(x=df_u[df_u['sentiment']=='Negative']['created_at'],y=df_u[df_u['sentiment']=='Negative']['nroll_7'], name='nroll_7')) user_bar.add_trace(go.Scatter(x=df_u[df_u['sentiment']=='Positive']['created_at'],y=df_u[df_u['sentiment']=='Positive']['proll_7'], name='proll_7')) url = py.plot(user_bar, filename=f'{user}_tweets_{freq}',auto_open=False) time.sleep(5) return tls.get_embed(url) except Exception as e: logger.error(f'plotly_url error: {e}')
def salaries(): jobs = pd.read_csv( r'C:\Users\lilyk\Desktop\Capstone_project-master\output.csv') salary = jobs['salary'].dropna() category = jobs['category'].dropna().head(200) salary = np.array(salary) titles = jobs['jobTitle'].dropna().head(200) sals = [] for i in range(len(salary)): test = (salary[i].split()) nums = [] for i in test: if i.startswith('$'): number = (i.strip('$')) number = number.replace(",", '') number = float(number) nums.append(number) else: sals.append(0.0) sals.append(statistics.mean(nums)) fig3 = px.bar(jobs, x=category, y=sals, color=sals, labels={ 'y': 'Salaries', 'x': 'Category' }, title='Salaries for Different Job Categories', height=400) tls.set_credentials_file(username='******', api_key='3ztc7kdqWPHtPtkhusiy') url = py.plot(fig3, filename='salFig', auto_open=True) return (tls.get_embed(url))
def edus(): jobs = pd.read_csv( r'C:\Users\lilyk\Desktop\Capstone_project-master\output.csv') edus = (jobs['education'].dropna()) edus = edus.str.split(',') #GETS THE UNIQUE EDUS u = [] for x in edus: for i in x: i = i.replace('bachelors', "bachelor's") u.append(i) countDict = (Counter(u)) vals = countDict.values() keys = countDict.keys() keys, values = zip(*countDict.items()) fig = px.bar(jobs, x=keys, y=values, color=keys, labels={ 'y': 'Frequency', 'x': 'Education' }, title='Most Desired Education Levels') #fig.show() #This puts it in your cloud for your Chart Studios Account #You can switch it to be your account/api_key from your account. tls.set_credentials_file(username='******', api_key='3ztc7kdqWPHtPtkhusiy') url = py.plot(fig, filename='eduFig', auto_open=True) return (tls.get_embed(url))
def map(self, Model): username = '******' api_key = 'OmWwvBMIO3rYLry5fn5F' tls.set_credentials_file(username=username, api_key=api_key) states = [ 'al', 'ak', 'az', 'ar', 'ca', 'co', 'ct', 'dc', 'de', 'fl', 'ga', 'hi', 'id', 'il', 'in', 'ia', 'ks', 'ky', 'la', 'me', 'md', 'ma', 'mi', 'mn', 'ms', 'mo', 'mt', 'ne', 'nv', 'nh', 'nj', 'nm', 'ny', 'nc', 'nd', 'oh', 'ok', 'or', 'pa', 'ri', 'sc', 'sd', 'tn', 'tx', 'ut', 'vt', 'va', 'wa', 'wv', 'wi', 'wy' ] locations = [state.upper() for state in states] df = self.df.copy() df = df.loc[df.index.repeat(len(states))].reset_index(drop=True) df['state'] = states preds = Model.model.predict(df) predictions = [int(pred) for pred in preds] fig = go.Figure(data=go.Choropleth( locations=locations, # Spatial coordinates z=predictions, # Data to be color-coded locationmode= 'USA-states', # set of locations match entries in `locations` colorscale='PRGn', colorbar_title="Thousands USD", )) fig.update_layout( title_text= f'Used {self.titleName()} in {self.condition.capitalize()} Condition Prices by State', geo_scope='usa', # limite map scope to USA ) return py.plot(fig, filename='usedCars_map', auto_open=False)
size_max=100, zoom=4, hover_data=['Combined_Key', 'Deaths', 'Population'], width=1800, height=1000) fig.show() import plotly plotly.__version__ import chart_studio.plotly as py import chart_studio.tools as tls usrname = 'ryanzy' key = 'Mh539mReURbPeNqyub42' tls.set_credentials_file(username=usrname, api_key=key) py.plot(fig, filename='fig1', auto_open=False) pip install geopandas==0.3.0 pip install pyshp==1.2.10 pip install shapely==1.6.3 pip install plotly-geo import colorlover as cl from IPython.display import HTML HTML(cl.to_html( cl.flipper()['seq']['3'] ))
def edu_sal(): jobs = pd.read_csv( r'C:\Users\lilyk\Desktop\Capstone_project-master\output.csv') #makes it so u can graph, but messes up data edus = (jobs['education'].fillna('Not Specified').head(235)) edus = edus.str.split(',') #GETS THE UNIQUE EDUS u = [] for x in edus: for i in x: i = i.replace('bachelors', "bachelor's") u.append(i) print(len(u)) #keys,values = zip(*countDict.items()) ##KEYS IS EDUS salary = jobs['salary'].dropna() salary = np.array(salary) sals = [] for i in range(len(salary)): # print (salary[i]) test = (salary[i].split()) nums = [] for i in test: if i.startswith('$'): number = (i.strip('$')) number = number.replace(",", '') #print (number) number = float(number) #print (number) nums.append(number) else: pass try: sals.append(statistics.mean(nums)) except: sals.append(nums) #print (sals) print(len(sals)) fig = px.scatter(jobs, x=u, y=sals, color=u, labels={ 'y': 'Sal', 'x': 'Education' }, title='edu/sal', size=sals) tls.set_credentials_file(username='******', api_key='3ztc7kdqWPHtPtkhusiy') url = py.plot(fig, filename='scatEduSal', auto_open=True) return (tls.get_embed(url))
def plot_karma_metric(allVotes, start_date, end_date, online=False, period='D', ma=7): votes_ts = allVotes.set_index('votedAt').resample(period)['effect'].sum() votes_ts = votes_ts.reset_index().iloc[:-1] votes_ts_ma = votes_ts.set_index('votedAt')['effect'].rolling( ma).mean().round(1).reset_index() days_in_period = {'D': 1, 'W': 7, 'M': 365 / 12, 'Y': 365} # trends = create_trend_frame(days_in_period[period] * 550, period) # plotly section date_col = 'votedAt' title = 'effect' color = 'red' size = (1200, 500) data = [ go.Scatter(x=votes_ts[date_col], y=votes_ts['effect'].round(1), line={ 'color': color, 'width': 0.5 }, name='{}-value'.format(PERIOD_DICT[period]), hoverinfo='x+y+name'), go.Scatter(x=votes_ts_ma[date_col], y=votes_ts_ma['effect'].round(1), line={ 'color': color, 'width': 4 }, name='average of last {} {}s'.format( ma, PERIOD_DICT2[period]), hoverinfo='x+y+name') #, # go.Scatter(x=trends['date'], y=trends['5%'], line={'color': 'grey', 'width': 1, 'dash': 'dash'}, mode='lines', # name='5% growth', hoverinfo='skip'), # go.Scatter(x=trends['date'], y=trends['7%'], line={'color': 'black', 'width': 2, 'dash': 'dash'}, mode='lines', # name='7% growth', hoverinfo='x+y'), # go.Scatter(x=trends['date'], y=trends['10%'], line={'color': 'grey', 'width': 1, 'dash': 'dash'}, mode='lines', # name='10% growth', hoverinfo='skip') ] layout = go.Layout( autosize=True, width=size[0], height=size[1], title='Net Karma, 4x Downvote, {}, 1.2 item exponent'.format( PERIOD_DICT[period].capitalize()), xaxis={ 'range': [start_date, end_date], 'title': None }, yaxis={ 'range': [ 0, votes_ts.set_index(date_col)[start_date:]['effect'].max() * 1.1 ], 'title': 'net karma' }) fig = go.Figure(data=data, layout=layout) set_credentials_file(username=get_config_field('PLOTLY', 'username'), api_key=get_config_field('PLOTLY', 'api_key')) init_notebook_mode(connected=True) filename = 'Net Karma Metric - {}'.format(PERIOD_DICT[period].capitalize()) if online: py.iplot(fig, filename=filename) else: iplot(fig, filename=filename) return votes_ts
#!/usr/bin/env python # -*- coding: utf-8 -*- import csv import numpy as np import pandas as pd #import plotly.tools as tls import chart_studio.tools as tls #import plotly.plotly as py import chart_studio.plotly as py import plotly.graph_objs as go tls.set_credentials_file(username='******', api_key='?????') listeDef = ['FULL', 'N0', 'S0'] listeFeuille = ['F3', 'F5'] minDas = 23 def organizeData(mat): """ fonction qui transforme les données en dictionnaire :param mat: les données importées du csv :return: les données sous forme dictionnaire """ print("\\\\\\\\\\\ Transform data to dict ////////////") data = [] for line in mat: dico = { 'Sample': line[0], 'Moda': line[1], 'Feuille': line[2],
import plotly import chart_studio.tools as plt_tools plt_tools.set_credentials_file(username="******", api_key="vEU6zehnSVEJ3oA8WqWA") import chart_studio.plotly as py import pandas as pd from plotly.subplots import make_subplots import plotly.graph_objs as go import plotly.express as px import sys import argparse def parseArgs(args): """ Wrapper function to parse script arguments :param args: script arguments :return: args: parsed arguments object """ parser = argparse.ArgumentParser( description="This script performs plotly plot generation.") parser.add_argument( "--input_csvs", "-ic", nargs='+', type=str, dest="input_csvs", default=[
"""Plotly chart creation.""" import plotly.graph_objects as go import chart_studio.plotly as py from chart_studio.tools import set_credentials_file from pandas import DataFrame from config import PLOTLY_API_KEY, PLOTLY_USERNAME # Plotly Chart Studio authentication set_credentials_file( username=PLOTLY_USERNAME, api_key=PLOTLY_API_KEY ) def create_chart(stock_df: DataFrame, symbol: str) -> py.plot: """Create Plotly chart from Pandas DataFrame.""" fig = go.Figure(data=[ go.Candlestick( x=stock_df.index, open=stock_df['open'], high=stock_df['high'], low=stock_df['low'], close=stock_df['close'], decreasing={ "line": { "color": "rgb(240, 99, 90)" }, "fillcolor": "rgba(142, 53, 47, 0.5)" }, increasing={ "line": {
def __init__(self, length=3, distance=0, seed=None): ''' Arguments: size (int): Determines the length of all cube faces. Ie, each face will have size^2 many squares. distance (int): The number of random moves used to generate the cube's starting position. seed (int): Optional argument that can be used to fix the random moves used to generate the position. When distance and seed are fixed, the same starting position will be generated. ''' self.face_colors = { 'front': 'yellow', 'right': 'red', 'back': 'green', 'left': 'orange', 'top': 'blue', 'bottom': 'white' } self.faces = list(self.face_colors.keys()) self.face_area = length**2 num_faces = 6 self.num_squares = num_faces * self.face_area self.face_indices = {self.faces[i]: i for i in range(num_faces)} self.color_labels = { color: color[0] for color in self.face_colors.values() } self.label_colors = { label: color for color, label in self.color_labels.items() } self.grid = list( itertools.product(np.arange(length), np.arange(length))) self.faces = np.array([[[ self.color_labels[self.face_colors[face]] for i in range(length) ] for j in range(length)] for face in self.face_colors]) self.front, self.right, self.back, self.left, self.top, self.bottom = self.faces[ 0:6, :, :] self.invert = lambda index: abs(2 - index) self.face_radius = 0.5 directions, dims, layers = [True, False], range(length), range(length) self.moves = list(itertools.product(dims, layers, directions)) # The None move entails doing nothing. It's included so that the search # algorithm isn't limited to finding solutions of the given search depth. self.moves.insert(0, None) self.compressions = [] self.face_to_axis = { 'front': 1, 'back': 1, 'left': 0, 'right': 0, 'top': 2, 'bottom': 2 } self.reward_hist = {} self.prev_moves = [] self.tried_moves = defaultdict(set) self.move_history = [] self.build_face_to_cartestian() self.x_dom = np.linspace(-3, 6, 10) self.y_dom = np.linspace(-3, 6, 10) self.z_dom = np.linspace(-3, 6, 10) self.input_faces = { 'xy': { True: [3, 0, 1, 2], False: [1, 2, 3, 0] }, 'xz': { True: [5, 0, 4, 2], False: [4, 2, 5, 0] } } self.output_faces = {'xy': range(4), 'xz': [0, 4, 2, 5]} self.input_cols = { 'xz': { True: lambda col: [col, col, col, self.invert(col)], False: lambda col: [self.invert(col), self.invert(col), col, col] } } self.output_cols = { 'xz': { True: lambda col: [col, col, self.invert(col), col], False: lambda col: [col, self.invert(col), self.invert(col), col] } } self.flip_faces = { 'xz': { True: lambda col: [col, self.invert(col)], False: lambda col: [self.invert(col), col] } } self.flip_cols = { 'xz': { True: lambda col: [col, self.invert(col)], False: lambda col: [self.invert(col), col] } } self.rotations = { tuple([0, 0, True]): ['top', -1], tuple([0, 0, False]): ['top', 1], tuple([0, 2, True]): ['bottom', -1], tuple([0, 2, False]): ['bottom', 1], tuple([1, 0, True]): ['left', 1], tuple([1, 0, False]): ['left', -1], tuple([1, 2, True]): ['right', -1], tuple([1, 2, False]): ['right', 1], tuple([2, 0, True]): ['front', 1], tuple([2, 0, False]): ['front', -1], tuple([2, 2, True]): ['back', -1], tuple([2, 2, False]): ['back', 1] } self.random_position(distance, seed) api_key = "MuCqsAcD75SBG4BSEuQx" tools.set_credentials_file(username='******', api_key=api_key)
import os from bottle import run, template, get, post, request from chart_studio.plotly import plot from chart_studio import tools from plotly.graph_objects import Bar tools.set_credentials_file(username='******', api_key='ECoh0rzizfQ8eUpUolkx') index_html = '''My first web app! By <strong>{{ author }}</strong>.''' @get('/plot') def form(): return '''<h2>Graph via Plot.ly</h2> <form method="POST" action="/plot"> Name: <input name="name1" type="text" /> Age: <input name="age1" type="text" /><br/> Name: <input name="name2" type="text" /> Age: <input name="age2" type="text" /><br/> Name: <input name="name3" type="text" /> Age: <input name="age3" type="text" /><br/> <input type="submit" /> </form>''' @post('/plot') def submit(): # grab data from form name1 = request.forms.get('name1') age1 = request.forms.get('age1') name2 = request.forms.get('name2')
import os import plotly.graph_objects as go import chart_studio.tools as chst_tl import chart_studio.plotly as chst_pl from dotenv import load_dotenv # import visualizer import data # user = visualizer.GithubUser() load_dotenv() chst_tl.set_credentials_file(username=os.getenv("CHART_STUDIO_USERNAME"), api_key=os.getenv("CHART_STUDIO_APIKEY")) class IndexPlot(): def __init__(self): pass def plot_k(self, group, index, period): # extract data if (group == "us_indexes"): holder = data.IndexDataHolder( "../finance_data/{group}/{id}.csv".format(group=group, id=index)) elif (group == "us_stocks"): holder = data.StockDataHolder( "../finance_data/{group}/{id}.csv".format(group=group,
import numpy as np import pandas as pd import chart_studio.plotly as py import chart_studio.tools as tls # credientials to access plotly map tls.set_credentials_file(username='******', api_key='9NS3Rm5M37XgJXMP1boe') # grab data from CDC covid data address = 'C:\\Users\\dejat\\OneDrive\\Documents\\covidthing.csv' states = pd.read_csv(address) states.columns = ['state', 'cases'] states.head() states['text'] = 'cases' + states['cases'].astype(str) data = [ dict(type='choropleth', autocolorscale=False, locations=states['state'], z=states['cases'], locationmode='USA-states', text=states['text'], colorscale='emrld', colorbar=dict(title="Number of cases")) ] layout = dict( title="Covid cases in USA", geo=dict(
meta_info = {"chart_name":"Consumer Price Index for All Urban Consumers","x_axis":"Date", "y_axis":"CPI", "series_id":"CPIAUCSL", "observation_start":"1995-01-01", "observation_end":FRED_date} try: fred = Fred(fred_api_key) # Login. data = fred.get_series(series_id=meta_info["series_id"], observation_start=meta_info["observation_start"], observation_end=meta_info["observation_end"]) meta_info["data"] = format_series(data, meta_info) charts.append(meta_info) print_and_pause('Saved', meta_info["chart_name"]) except: cleanup(f"Error retreiving and/or parsing series: {meta_info['chart_name']}.") #################### SHIP DATA #################### # Log into the Plotly account. try: tls.set_credentials_file(plotly_username, plotly_api_key) print_and_pause("Logging into", "Plotly API") except: cleanup("Error logging into plotly.") # Submit each item to plotly cloud. for item in charts: df = item["data"] fig = px.line(df, x=item['x_axis'], y=item["y_axis"], title=item["chart_name"]) # Plot item. py.plot(fig, filename=item["chart_name"], auto_open=False) # Ship item off to the Plotly API. print_and_pause('Shipped', item["chart_name"]) # Confirmation of completion. if run_without_errors: cleanup("Done")
def run_plotline(dfs, online=False, start_date=None, end_date=None, size=(1000, 400), pr='D', ma=[1, 7], widths={ 1: 0.75, 7: 3 }, annotations=False, hidden_by_default=[]): set_credentials_file(username=get_config_field('PLOTLY', 'username'), api_key=get_config_field('PLOTLY', 'api_key')) init_notebook_mode(connected=True) dpv = dfs['views'] # pv = post-views minimum_post_views = 1 valid_users = get_valid_users( dfs, required_minimum_posts_views=minimum_post_views) valid_posts = get_valid_posts(dfs, required_upvotes=1) valid_comments = get_valid_comments(dfs) valid_votes = get_valid_votes(dfs) valid_views = get_valid_views(dfs) valid_views['hour'] = valid_views['createdAt'].dt.round('H') valid_views_deduped = valid_views.drop_duplicates( subset=['userId', 'documentId', 'hour']) plotly_args = { 'start_date': start_date, 'end_date': end_date, 'period': pr, 'moving_average_lengths': ma, 'widths': widths, 'size': size, 'online': online, 'annotations': annotations, 'hidden_by_default': hidden_by_default } timeseries_plot( title='Accounts Created, {}+ posts_viewed'.format(minimum_post_views), datapoints=valid_users, date_col='true_earliest', color='grey', **plotly_args) timeseries_plot(title='Num Logged-In Users', datapoints=dpv[dpv['userId'].isin(valid_users['_id'])], date_col='createdAt', color='black', unique_on='userId', **plotly_args) timeseries_plot(title='Num Posts with 2+ upvotes', datapoints=valid_posts, date_col='postedAt', color='blue', **plotly_args) timeseries_plot(title='Num Unique Posters', datapoints=valid_posts, date_col='postedAt', color='darkblue', unique_on='userId', **plotly_args) timeseries_plot(title='Num Comments', datapoints=valid_comments, date_col='postedAt', color='green', **plotly_args) timeseries_plot(title='Num Unique Commenters', datapoints=valid_comments, date_col='postedAt', color='darkgreen', unique_on='userId', **plotly_args) timeseries_plot(title='Num Votes (excluding self-votes)', datapoints=valid_votes, date_col='votedAt', color='orange', **plotly_args) timeseries_plot(title='Num Unique Voters', datapoints=valid_votes, date_col='votedAt', color='darkorange', unique_on='userId', **plotly_args) timeseries_plot(title='Num Logged-In Post Views', datapoints=valid_views_deduped, date_col='createdAt', color='red', **plotly_args)
# In[ ]: from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador from google.colab import drive # Montar tu Google drive # # Gráficos con PlotLy # In[1]: get_ipython().system('pip install chart_studio') import chart_studio.plotly as py import plotly.graph_objects as go from chart_studio import tools as tls tls.set_credentials_file(username='******', api_key='6mEfSXf8XNyIzpxwb8z7') # In[2]: import plotly plotly.__version__ # In[5]: help(plotly) # In[8]: import numpy as np help(np.random)
" States with Largest Increase from 1996 to 2019", xticks=range(0, len(df_yearAgg))) plt.legend(title="State", bbox_to_anchor=(1, 1)) plt.xticks(rotation=90) plt.grid(True) plt.xlabel('By Year') plt.ylabel('Average Price, in thousands') plt.show() # US states heatmap import chart_studio.plotly as py import chart_studio.tools as tls df_rise_stateAgg.reset_index(level=0, inplace=True) tls.set_credentials_file(username='******', api_key='RsKQETjmHeHXwG8oM71w') df_rise_stateAgg[ 'Text'] = '' #'States' + df_rise_stateAgg['State'] + '<br>' + 'Average Price' + df_rise_stateAgg['2019-05'].astype(str) data = [dict(type = 'choropleth', autocolorscale = False, locations = df_rise_stateAgg['State'], z = df_rise_stateAgg['2019-06'],\ locationmode = 'USA-states', text = df_rise_stateAgg['Text'], colorscale = 'Portland',\ colorbar = dict(title = 'Average Housing Price'))] layout = dict( title="2-Bedroom Average Housing Price in June 2019, in thousands", geo=dict(scope='usa', projection=dict(type='albers usa'))) fig = dict(data=data, layout=layout) py.plot(fig, filename="Housing Price")
import os import numpy as np import pandas as pd from tqdm.notebook import tqdm import plotly.graph_objects as go import chart_studio.plotly as py import chart_studio.tools as tls # Log into Chart studio for visual chart upload tls.set_credentials_file(username='******', api_key='••••••••') # Custom function for pretty-printing a number with comma-separators and/or decimal vals def comma_num(x, dollars=0, dec=0): if dec: x = round(x, dec) elif dollars: x = round(x) minus = False x = str(x) if '-' in x: minus = True x = x.replace('-', '').strip() parts = [list(l) for l in x.split('.')] predec = parts[0] if len(parts) > 1: postdec = parts[-1] else: postdec = None if len(predec) > 3: for i in list(range(-3, -len(predec) - 1, -4)):
import gspread import random import chart_studio.plotly as py from chart_studio import tools from datetime import time # Plotly Credentials tools.set_credentials_file(username='******', api_key='INSERT_YOURS_HERE') # updated oauth from google.oauth2.service_account import Credentials # Express time in twenty-four hour format OpenVoteHour = 20 # the hour the poll opens OpenVoteMin = 50 # the minute the poll opens CloseVoteHour = 22 # the hour the poll closes CloseVoteMin = 0 # the minute the poll closes # Not Sure a better way to Map Human Readable to Code # Also, zero index ;) ChoiceMap = { u"First Choice": 0, u"Second Choice": 1, u"Third Choice": 2, u"Fourth Choice": 3, u"Fifth Choice": 4, u"Sixth Choice": 5, u"Seventh Choice": 6, u"Eighth Choice": 7, u"Ninth Choice": 8,