def generate_Z_msr_org(numOfBuses, numOfLines, bus_data_df, topo_mat, file_name): import pandas as pd import numpy as np import openpyxl from openpyxl import load_workbook # Creating Measurement Data to run state estimation bus_data = bus_data_df[[ 'Remote controlled bus number', 'Load MW', 'Generation MW' ]] bus_data.columns = ['Bus number', 'Load', 'Generation'] # Correcting the load generation for a lossless DC system correction_load = sum(bus_data['Load']) - sum(bus_data['Generation']) print("correction_load: ", correction_load) # Adding the correction load to the largest generator bus_data['Generation'].loc[ bus_data['Generation'].idxmax()] += correction_load # correction_check = sum(bus_data['Load']) - sum(bus_data['Generation']) # print("correction_check: ", correction_check) # Bus Power = Bus Gen - Bus Load bus_data['Bus Power'] = bus_data['Generation'] - bus_data['Load'] print("bus_data:\n", bus_data.head()) # Padding 0 in the top of the data from reference Z_data_bus_power = pd.DataFrame( pd.concat([pd.Series([0]), bus_data['Bus Power']])) # Topomat containing only the bus power rows along with reference bus B_mat_bus_power = pd.concat( [topo_mat.loc[0:0], topo_mat.loc[numOfLines * 2 + 1:]]) # Estimating the states fromt the bus power data state_original = np.linalg.pinv(B_mat_bus_power) @ Z_data_bus_power # Calculating the Z_msr_org using the Topology Matrix and states Z_msr_org = topo_mat @ state_original Z_msr_org.columns = ['Data'] # Saving the data book = load_workbook(file_name) writer = pd.ExcelWriter(file_name, engine='openpyxl') writer.book = book Z_msr_org.to_excel(writer, "Measurement Data", index=False) bus_data.to_excel(writer, "Bus Data", index=False) writer.save() writer.close() # saving complete ! print("Z_msr_org:\n", Z_msr_org.head()) return Z_msr_org, bus_data
def test_init_example_module(self): """Ensures that the twine class can be instantiated with a file""" # test_data_file = self.path + "test_data/.json" df = pd.DataFrame({ "attribute": ["A", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "B"], "value": [1, 2, 4, 5, 3, 6, 100, 33, 44, 77, 77, 99], }) gvt = GroupedVariableTransformation(key="attribute", target="value") gvt.fit(df) print(gvt) gvt.transform(df)
def topologyProcessor(numOfBuses, numOfLines, line_data): import pandas as pd import numpy as np import openpyxl from openpyxl import load_workbook numOfZ = numOfBuses + numOfLines * 2 # Placeholder for topoMat topo_mat = pd.DataFrame(np.zeros((numOfZ + 1, numOfBuses), dtype=float)) # rows representing the line powerflow for line in line_data.values: # Line information lineID = int(line[0]) fromBus = int(line[1] - 1) toBus = int(line[2] - 1) admittance = line[3] # topo_mat.iloc[lineID, fromBus] = admittance topo_mat.iloc[lineID, toBus] = -admittance topo_mat.iloc[lineID + numOfLines, fromBus] = -admittance topo_mat.iloc[lineID + numOfLines, toBus] = admittance # rows representing the bus consumption for busIndx in range(1, numOfBuses + 1): busTopo = np.zeros(numOfBuses) for line in line_data.values: # Line information lineID = int(line[0]) fromBus = int(line[1]) toBus = int(line[2]) if fromBus == busIndx: busTopo = busTopo + topo_mat.loc[lineID] elif toBus == busIndx: busTopo = busTopo - topo_mat.loc[lineID] topo_mat.loc[2 * numOfLines + busIndx] = busTopo.copy() # adding 1 in the first line which represents the reference bus topo_mat.iloc[0, 0] = 1 return topo_mat
def review_count_scrape(): url ='https://www.amazon.com/Best-Sellers/zgbs' headers = ({'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537', 'Accept-Language': 'en-US, en; q=0.5''}) r = requests.get(url, headers=headers) soup = BeautifulSoup(r.text,'Lxml') print(r.status_code) product_total_review= [i.text for i in soup.findAll('a', {'class': 'a-small a-link normall'}] df =pd.DataFrame(product_total_review) print (df) time.sleep(60) end_timer =time.time() +60 * 2 while time.time() < end t
def xlm_to_csv(path): xml_list = [] for xml_file in glob.glob(path + '/".xml"'): tree = ET.parse(xml_file) root = tree.getroot() for member in root.findall('object'): value = ( 'data/' + root.find('filename').text, int(member[4][0].text), int(member[4][1].text), int(member[4][2].text), int(member[4][3].text), member[0].text, ) xml_list.append(value) column_name = ['filename', 'xmin', 'ymin', 'xmax', 'ymax', 'class'] xml_df = pd.DataFrame(xml_list, column=column_name) return xml_df
# -*- coding: utf-8 -*- """ Created on Mon Jan 15 15:04:07 2018 @author: david """ import panda as pd import numpy as np df = pd.DataFrame({ 'rest_name': ['Salty Sow'], 'rating': [4.5], 'reviews_num': [1968] })
def textprocessing(): commentList = [] dateList = [] for i in range(10): num = i + 1 [commentList_temp, dateList_temp] = getComments(num) commentList.append(commentList_temp) dateList.append(dateList_temp) commentList = reduce(operator.add, commentList) dateList = reduce(operator.add, dateList) dataTmp = {'comments': commentList[:], 'date': dateList[:]} df2 = pd.DataFrame(dataTmp) pd.DataFrame(df2).to_excel("text-movie.xls", sheet_name="sheet1", index=False, header=True) comments = '' for k in range(len(commentList)): comments = comments + (str(commentList[k])).strip() pattern = re.compile(r'[\u4e00-\u9fa5]+') filterdata = re.findall(pattern, comments) # 过滤标点 用正则表达式 cleaned_comments = ''.join(filterdata) seg_list_exact = jieba.cut(cleaned_comments, cut_all=False) # 精确模式分词 object_list = [] remove_words = pd.read_csv("stopwords.txt", index_col=False, quoting=3, sep="\t", names=['stopword'], encoding='utf-8') for word in seg_list_exact: # 循环读出每个分词 if word not in remove_words: # 如果不在去除词库中 object_list.append(word) # 分词追加到列表 # 词频统计 word_counts = collections.Counter(object_list) # 对分词做词频统计 word_counts_top10 = word_counts.most_common(10) # 获取前10最高频的词 print(word_counts_top10) # 输出检查 # 词频展示 mask = np.array(Image.open('background.jpg')) # 定义词频背景 wc = wordcloud.WordCloud( background_color='white', # 设置背景颜色 font_path='/System/Library/Fonts/Hiragino Sans GB.ttc', # 设置字体格式 mask=mask, # 设置背景图 max_words=200, # 最多显示词数 max_font_size=100, # 字体最大值 scale=32 # 调整图片清晰度,值越大越清楚 ) wc.generate_from_frequencies(word_counts) # 从字典生成词云 image_colors = wordcloud.ImageColorGenerator(mask) # 从背景图建立颜色方案 wc.recolor(color_func=image_colors) # 将词云颜色设置为背景图方案 wc.to_file("/Users/ownpro/Desktop/temp.jpg") # 将图片输出为文件 plt.imshow(wc) # 显示词云 plt.axis('off') # 关闭坐标轴 plt.show() # 显示图像
data_length = len(mylist) data_list = [] # Convert the strings to dictionaries import json for i in mylist: d = json.loads(i) data_list.append(d) list_keys = list(data_list[0].keys()) # The key_listed_used can vary for different tasks # here just use three features as a demo key_list_used = ['all_ratio', 'platform', 'genres'] value_list1 = [] value_list2 = [] value_list3 = [] key1 = key_list_used[0] key2 = key_list_used[1] key3 = key_list_used[2] for i in data_list: # features w/o any values return None value_list1.append(i.get(key1)) value_list2.append(i.get(key2)) value_list3.append(i.get(key3)) value_list_used = [value_list1, value_list2, value_list3] dic_used = dict(zip(key_list_used, value_list_used)) # Convert to DataFrame import panda as pd df_used = pd.DataFrame(dic_used)
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame( { "State":["Andaman And Nicobar", "Andhra Pradesh","Arunachal Pradesh", ] } )
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame({})
@author: arti """ import googlemaps import panda as pd my_key = "" maps = googlemaps.Client(key=my_key) lat = [] lng = [] places = ["서울시청", "국립국악원", "해운대해수욕장"] i = 0 for place in places: i = i + 1 try: print(i, place) geo_location = maps.geocode(place)[0].get('geometry') lat.append(geo_location['location']['lat']) lng.append(geo_location['location']['lng']) except: lat.append('') lng.append('') print(i) df = pd.DataFrame({'위도': lat, '경도': lng}, index=places) print(df)
import panda as pd import os import time import gps_data_read as gps ret, gps_loc_data = gps.GPS_Read_Data_RMC() gps_dist_present = None path = os.getcwd() + "/gps_data_csv" if (not os.path.exists(path)): os.makedirs(path) try: os.chdir(path) except NameError as e: print("Can not access path") exit() i = 0 while ret == True: try: ret, gps_loc_data.append(gps.GPS_Read_Data_RMC()) gps_dist_present.append( gps.dist_calc_present(gps_loc_data[i][0], gps_loc_data[i][2])) except KeyboardInterrupt as e: data_set = list(zip(gps_loc_data, gps_dist_present)) df = pd.DataFrame(data=data_set, columns=None) df.to_csv("gps_data_set.csv", index=False, header=False)
#prob = cv2.resize(pred, (orig_width, orig_height)) prob = pred mask = prob > threshold ### imshow ### A little animation -start- cv2.imshow('image',prob) cv2.waitKey(1000) ### 100 msec for each ### A little animation -end- rle = run_length_encode(mask) rles.append(rle) cv2.destroyAllWindows() ### Submit to Kaggle print("Generating submission file...") df = pd.DataFrame({'img': test_names, 'rle_mask': rles}) df.to_csv('submit/submission.csv.gz', index=False, compression='gzip') ''' gif2png.py from PIL import Image kaggle_train_mask_path = 'Kaggle_Car_Data/train_masks/train_masks' for filename in os.listdir(kaggle_train_mask_path): if filename.endswith(".gif"): print(filename) im_mask = Image.open(kaggle_train_mask_path + '/' + filename) png_filename = filename[:-4] + '.png' im_mask.save(kaggle_train_mask_path + '/' + png_filename,"PNG") ,,,
def get_unsorted_all_commits_dates(self): rev = sys.argv[1] cumulative = 0 if len(sys.argv) == 4: if (sys.argv[3] == "c"): cumulative = 1 else: print("Dont know what you mean with %s" % sys.argv[3]) sys.exit(-1) rev_range = int(sys.argv[2]) print("#sublevel commits %s stable fixes" % rev) print("lv hour bugs") #tag for R data.frame rev1 = rev v44 = 1452466892 try: for sl in range(1, rev_range + 1): rev2 = rev + "." + str(sl) gitcnt = self.gitcnt + rev1 + "..." + rev2 gittag = self.gittag + rev2 git_rev_list = Popen(gitcnt, stdout=PIPE, stderr=DEVNULL, shell=True) commit_cnt = self.get_commit_cnt(git_rev_list) if cumulative == 0: rev1 = rev2 # if get back 0 then its an invalid revision number sl_list = [] days_list = [] commits_cnt_list = [] if commit_cnt: git_tag_date = Popen(gittag, stdout=PIPE, stderr=DEVNULL, shell=True) days = self.get_tag_days(git_tag_date, v44) sl_list.append(sl) days_list.append(days) commits_cnt_list.append(commit_cnt) print("%d %d %d" % (sl, days, commit_cnt)) else: print('Its an invalid revision number') break # create dataframe re = np.array([sl_list, days_list, commits_cnt_list]) data = re.transpose() print(data) c = ["1v", "hour", "bugs"] df = pd.DataFrame(data=data, columns=c) # write in csv file data_file = 'data_v4.4' csv_data = data_file + ".csv" df.to_csv(csv_data) except: raise ValueError('something wrong.')
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame({ "State": [ "Andaman And Nicobar", "Andhra Pradesh", "Arunachal Pradesh", "Assam", "Bihar", "Chandigarh", "Chhattisgarh", ] })
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame({"State": []})
#reading dataset data = pd.read_csv('/root/task5/pro.csv') #dropping the unusefull data = data.dropna() data = data.drop(['url'], axis='columns',implace=True) ip= data['IP'] count=Counter(ip) #scalling the dataset sc = StandardScaler() data_scaled = sc.fit_transform(dataset) model = KMeans(n_clusters=4) #fitting the model model.fit(data_scaled) pred = model.fit_predict(data_scaled) dataset_scaled = pd.DataFrame(data_scaled, columns=['IP', 'c']) pred=dataset_scaled['cluster'] #plotting the clusters f1 = data[data.cluster==0] f2 = data[data.cluster==1] f3 = data[data.cluster==2] f4 = data[data.cluster==3] plt.scatter(f1.count,f1['IP'],color='green') plt.scatter(f2.count,f2['IP'],color='red') plt.scatter(f3.count,f3['IP'],color='black') plt.scatter(f4.count,f4['IP'],color='blue') plt.scatter(km.cluster_centers_[:,0],km.cluster_centers_[:,1],km.cluster_centers_[:,2],color='purple',marker='*',label='centroid') plt.xlabel('Count') plt.ylabel('IP') plt.legend()
corona_dataset_csv.head(5) #Grouping by the country corona_dataset_csv.groupby("Countr/Region").sum() #Visualising data related to country corona_dataset_csv.loc["India"] #Compare the data by country with the help of Plotting corona_dataset_csv.loc["India"].plot() corona_dataset_csv.loc["China"].plot() corona_dataset_csv.loc["Italy"].plot() plt.legend() #For Plotting the rise by the desired date corona_dataset_csv.loc["India"][:10].plot() #Calculating the max. spike of the covid-19 case: corona_dataset_csv.loc["India"].diff().plot() #Finding the max. derivative: countries = list(corona_dataset_csv.index) max_infection_rates = [] for c in countries: max_infection_rates.append(corona_dataset_csv.loc[c].diff().max()) corona_dataset_csv['max infection rate'] = max_infection_rates #Creating a new Dataframe with needed Dataset corona_data = pd.DataFrame(corona_dataset_csv['max infection rate']) corona_data.head()
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame({"State": ["Andaman And Nicobar"]})
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame( { "State":["Andaman And Nicobar", "Andhra Pradesh","Arunachal Pradesh","Assam", "Bihar", "Chandigarh", "Chhattisgarh", "Dadra And Nagar Haveli", "Delhi", "Goa", "Gujarat", "Haryana", "Himachal Pradesh", "Jammu And Kashmir", "Jharkhand", "Karnataka", ] } )
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame( { "State":["Andaman And Nicobar", "Andhra Pradesh","Arunachal Pradesh","Assam", "Bihar", "Chandigarh", "Chhattisgarh", "Dadra And Nagar Haveli", "Delhi", "Goa", "Gujarat", "Haryana", "Himachal Pradesh", "Jammu And Kashmir", "Jharkhand", "Karnataka", "Kerala", "Lakshadweep", "Madhya Pradesh", "Maharashtra", "Manipur", "Meghalaya", "Mizoram", "Nagaland", "Orissa", "Puducherry", "Punjab", "Rajasthan", "Sikkim", "Tamil Nadu", "Tripura", "Uttar Pradesh", "Uttarakhand", "West Bengal"], "Population":[380581, 84580777, 1383727, 31205576, 104099452, 1055450, 25545198, 586956, 16787941, 1458545, 60439692, 25351462, 6864602, 12541302, 32988134, 61095297, 33406061, 64473, 72626809, 112374333, 2855794, 2966889, 1097206, 1978502, 41974218, 1247953, 27743338, 68548437, 610577, 72147030, 3673917, 199812341, 10086292, 91276115] } ) fig = px.bar(df, x="State", y="Population", color="State", barmode="group") app.layout = html.Div(children=[ html.H1(children='Hello Dash'), html.Div(children=''' Dash: A web application framework for Python. '''), dcc.Graph( id='example-graph',
#!/usr/bin/env python # -*- coding: utf-8 -*- from pystocktwits_data_utils import PyStockTwitData import panda as pd data = PyStockTwitData() # Get all msgs from this company that is specified list_of_msgs, list_of_sentiment_json = ( data.get_all_msgs_with_sentiment_by_symbol_id("VEEV")) # Parse out the Bullish, Bearish, or None Sentiment list_of_sentiment = ( data.extract_sentiment_statements_basic(list_of_sentiment_json)) # Create a Dataframe dataframe = pd.DataFrame({'msg': list_of_msgs, 'sentiment': list_of_sentiment}) # Print to see dataframe and save print(dataframe) dataframe.to_csv('../sample_csv_output/pystockdataset.csv')
app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame({ "State": [ "Andaman And Nicobar", "Andhra Pradesh", "Arunachal Pradesh", "Assam", "Bihar", "Chandigarh", "Chhattisgarh", "Dadra And Nagar Haveli", "Delhi", "Goa", "Gujarat", "Haryana", "Himachal Pradesh", "Jammu And Kashmir", "Jharkhand", "Karnataka", "Kerala", "Lakshadweep", "Madhya Pradesh", "Maharashtra", "Manipur", "Meghalaya", "Mizoram", "Nagaland", "Orissa", ] })
import numpy as np import panda as pd np.random.seed( 101) # Todos os numeros que gerar vão ser iguais em todo computador df = pd.DataFrame(np.random.randn(5, 4), index="A B C D E".split(), columns="W X Y Z".split()) bol = df > 0 #vai retornar uma tabela de booleanos df[bol] # Numeros onde true, NaN onde false df(df['W'] > 0) #Nesse caso, vai excluir as linhas onde não atende. Retorna em numeros df[df['W'] > 0][ 'Y'] #Pega a coluna Y, mas só das linhas onde o W é maior que zero df[(df['W'] > 0) & (df['Y'] > 1)] # 'and' só compara booleanos, tem que ser o & df.reset_index() #Adiciona uma coluna de números ao lado de A, B, C... df.set_index('W') #W vai virar o índice
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame({ "State": [ "Andaman And Nicobar", "Andhra Pradesh", "Arunachal Pradesh", "Assam", "Bihar", "Chandigarh", "Chhattisgarh", "Dadra And Nagar Haveli", "Delhi", "Goa", "Gujarat", "Haryana", "Himachal Pradesh", "Jammu And Kashmir", "Jharkhand", "Karnataka", "Kerala", "Lakshadweep", "Madhya Pradesh", "Maharashtra", "Manipur", "Meghalaya", "Mizoram", "Nagaland", "Orissa", "Puducherry", "Punjab", "Rajasthan", "Sikkim", "Tamil Nadu", "Tripura", "Uttar Pradesh", "Uttarakhand", "West Bengal" ], "Population": [ 380581, ] })
finally, score with MAE ''' # 1) drop columns cols_with_missing_values = [ col for col in train_X.columns if train_X[col].isnull().any() ] reduced_train_x = train_X.drop(cols_with_missing_values, axis=1) reduced_val_x = val_X.drop(cols_with_missing_values, axis=1) # 2) imputation from sklearn.impute import SimpleImputer my_imputer = SimpleImputer() imputed_train_x = pd.DataFrame(my_imputer.fit_transform(train_X)) imputed_val_x = pd.DataFrame(my_imputer.fit_transform(val_X)) # imputation remove column names; adding it back imputed_train_x.columns = train_X.columns imputed_val_x.columns = val_X.columns # print shape of data (num_rows, num_columns) train_X.shape() # print number of missing values in each column of training data missing_val_count_by_column = train_X.isnull().sum( ) # returns all column with sum of null missing_val_count_by_column[missing_val_count_by_column > 0] # returns column names if sum > 0 (bool) '''Categorical Variables
InteractiveShell.ast_node_interactivity = "all" train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.25, random_state=1) train_x.shape test_x.shape train_y.shape test_y.shape linear_model = LinearRegression() linear_model linear_model.fit(train_x, train_y) test_prediction = linear_model.predict(test_x) print(linear_model.coef_) df_model = pd.DataFrame({'features': x.columns, 'coeff': linear_model.coef_}) df_model = df_model.sort_values(by=['coeff']) df_model df_model.plot(x='features', y='coeff', kind='bar', figsize=(15, 10)) plt.show() fdf = pd.concat([test_x, test_y], 1) fdf['Predicted'] = np.round(predict_test, 1) fdf['Prediction_Error'] = fdf[''] - fdf['Predicted'] # Add something for fdf[''] - maybe fdf['Death'] fdf
#read and write to csv file df = pd.read_csv('file_name') df = df.to_csv('example', index=False) #Excel Input and output, beware of image in the excel file, it may cause it to crash pd.read_excel('Excel_Sample.xlsx',sheetname='Sheet1') pd.to_excel('excelname.xlsm', sheetname) #Html Input df = pd.read_html('http://.....html') #Read Database in sql from sqlalchemy import create_engine engine = create_engine('sqlite:///:momory:') df.to_sql('data', engine) sql_df = pd.read_sql('data', con=engine) #Convert to DataFrames df= pd.DataFrame(np.random(5,4), index='A B C D E'.split(), columns='W X Y Z'.split()) df['W'] or df['W', 'Z'] #to call one or more column #creating new columns df['new'] = #whatever #Removing columns df.drop('nameofthecolumn',axis=1) #need to use "inplace=True" to make it inplace change #selecting Rows df.loc['A'] #using the name to location row df.iloc[2] #using index to location the row df.loc['B','Y'] #locate a item in table df.loc[['A', 'B', ['W', 'Y']]] #selecting a box of element #can also use condition to filter data df[df>0] df[df['W']>0] #reset to index base row
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame( { "State":["Andaman And Nicobar", "Andhra Pradesh","Arunachal Pradesh","Assam", "Bihar", "Chandigarh", "Chhattisgarh", "Dadra And Nagar Haveli", "Delhi", "Goa", "Gujarat", "Haryana", "Himachal Pradesh", "Jammu And Kashmir", "Jharkhand", "Karnataka", "Kerala", "Lakshadweep", "Madhya Pradesh", "Maharashtra", "Manipur", "Meghalaya", "Mizoram", "Nagaland", "Orissa", "Puducherry", "Punjab", "Rajasthan", "Sikkim", "Tamil Nadu", "Tripura", "Uttar Pradesh", "Uttarakhand", "West Bengal"], "Population":[380581, 84580777, 1383727, 31205576, 104099452, 1055450, 25545198, 586956, 16787941, 1458545, 60439692, 25351462, 6864602, 12541302, 32988134, 61095297, 33406061, ] } )
import dash import dash_core_components as doc import dash_html_components as html import plotly.express as px import panda as pd external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css'] app = dash.Dash(__name__, external_stylesheets=external_stylesheets) df = pd.DataFrame({""})