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Coursera Course - Introduction of Data Science in Python Assignment 1
I'm taking this course on Coursera, and I'm running some issues while doing the first assignment. The task is to basically use regular expression to get certain values from the given file. Then, the function should output a dictionary containing these values:
This is just a screenshot of the file. Due to some reasons, the link doesn't work if it's not open directly from Coursera. I apologize in advance for the bad formatting. One thing I must point out is that for some cases, as you can see in the first example, there's no username. Instead '-' is used.
This is what I currently have right now. However, the output is None. I guess there's something wrong in my pattern.
2 Answers 2
You can use the following expression:
See the regex demo . See the Python demo :
- Thank you so much!!! It worked!!! However, may I just ask a question regarding your solution? It probably sounds stupid, but don't you need to include everything in the parenthesis? For example, ("?P<request>[^"]*"). Or are they the same? Also, may you please explain the meaning of "?:" in your regular expression – BryantHsiung Commented Oct 19, 2020 at 13:20
- @BryantHsiung You can't use ("?P<request>[^"]*") , it is an invalid regex construct. See more about non-capturing groups here . – Wiktor Stribiżew Commented Oct 19, 2020 at 13:42
- 1 Just did! Thanks again! – BryantHsiung Commented Oct 20, 2020 at 12:42
- 1 I am working on the same question but I don't know why my for loop doesn't give me an output! I check my regex pattern on regex101 and they are all seem to be working the way they should. – Anoushiravan R Commented Jan 9, 2022 at 20:22
- @AnoushiravanR Without seeing your code, I can't help. – Wiktor Stribiżew Commented Jan 9, 2022 at 20:29
Check using following code:
For more information regarding regex, read the following documentation, it would be very useful for beginners: https://docs.python.org/3/library/re.html#module-re
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| Assignment 3 - More Pandas | |
| This assignment requires more individual learning then the last one did - you are encouraged to check out the pandas documentation to find functions or methods you might not have used yet, or ask questions on Stack Overflow and tag them as pandas and python related. And of course, the discussion forums are open for interaction with your peers and the course staff. | |
| Question 1 (20%) | |
| Load the energy data from the file Energy Indicators.xls, which is a list of indicators of energy supply and renewable electricity production from the United Nations for the year 2013, and should be put into a DataFrame with the variable name of energy. | |
| Keep in mind that this is an Excel file, and not a comma separated values file. Also, make sure to exclude the footer and header information from the datafile. The first two columns are unneccessary, so you should get rid of them, and you should change the column labels so that the columns are: | |
| ['Country', 'Energy Supply', 'Energy Supply per Capita', '% Renewable'] | |
| Convert Energy Supply to gigajoules (there are 1,000,000 gigajoules in a petajoule). For all countries which have missing data (e.g. data with "...") make sure this is reflected as np.NaN values. | |
| Rename the following list of countries (for use in later questions): | |
| "Republic of Korea": "South Korea", | |
| "United States of America": "United States", | |
| "United Kingdom of Great Britain and Northern Ireland": "United Kingdom", | |
| "China, Hong Kong Special Administrative Region": "Hong Kong" | |
| There are also several countries with numbers and/or parenthesis in their name. Be sure to remove these, | |
| e.g. | |
| 'Bolivia (Plurinational State of)' should be 'Bolivia', | |
| 'Switzerland17' should be 'Switzerland'. | |
| Next, load the GDP data from the file world_bank.csv, which is a csv containing countries' GDP from 1960 to 2015 from World Bank. Call this DataFrame GDP. | |
| Make sure to skip the header, and rename the following list of countries: | |
| "Korea, Rep.": "South Korea", | |
| "Iran, Islamic Rep.": "Iran", | |
| "Hong Kong SAR, China": "Hong Kong" | |
| Finally, load the Sciamgo Journal and Country Rank data for Energy Engineering and Power Technology from the file scimagojr-3.xlsx, which ranks countries based on their journal contributions in the aforementioned area. Call this DataFrame ScimEn. | |
| Join the three datasets: GDP, Energy, and ScimEn into a new dataset (using the intersection of country names). Use only the last 10 years (2006-2015) of GDP data and only the top 15 countries by Scimagojr 'Rank' (Rank 1 through 15). | |
| The index of this DataFrame should be the name of the country, and the columns should be ['Rank', 'Documents', 'Citable documents', 'Citations', 'Self-citations', 'Citations per document', 'H index', 'Energy Supply', indicators.xls 'Energy Supply per Capita', '% Renewable', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015']. | |
| This function should return a DataFrame with 20 columns and 15 entries. | |
| import pandas as pd | |
| import numpy as np | |
| def answer_one(): | |
| #file='Energy Indicators.xls' | |
| energy=pd.read_excel('Energy Indicators.xls') | |
| energy=energy[16:243] | |
| energy.drop(['Unnamed: 0','Unnamed: 1'],axis=1,inplace=True) | |
| energy=energy.rename(columns={'Environmental Indicators: Energy':'Country','Unnamed: 3':'Energy Supply','Unnamed: 4':'Energy Supply per Capita','Unnamed: 5':'% Renewable'}) | |
| energy=energy.replace('...',np.NaN) | |
| energy['Energy Supply']*=1000000 | |
| energy['Country'] = energy['Country'].str.replace('\d+', '') | |
| def braces(data): | |
| i = data.find('(') | |
| if i>-1: data = data[:i] | |
| return data.strip() | |
| energy['Country']=energy['Country'].apply(braces) | |
| d={"Republic of Korea": "South Korea", | |
| "United States of America": "United States", | |
| "United Kingdom of Great Britain and Northern Ireland": "United Kingdom", | |
| "China, Hong Kong Special Administrative Region": "Hong Kong", | |
| "Bolivia (Plurinational State of)":"Bolivia", | |
| "Switzerland17":"Switzerland"} | |
| energy.replace({"Country": d},inplace=True) | |
| GDP=pd.read_csv('world_bank.csv',skiprows=4) | |
| GDP.replace({"Korea, Rep.": "South Korea", | |
| "Iran, Islamic Rep.": "Iran", | |
| "Hong Kong SAR, China": "Hong Kong"},inplace=True) | |
| GDP.rename(columns={'Country Name':'Country'},inplace=True) | |
| ScimEn=pd.read_excel('scimagojr-3.xlsx') | |
| df1=pd.merge(energy,GDP,how='inner',left_on='Country',right_on='Country') | |
| df=pd.merge(df1,ScimEn,how='inner',left_on='Country',right_on='Country') | |
| outer=pd.merge(pd.merge(energy,GDP,how='outer',on='Country'),ScimEn,how='outer',on='Country') | |
| df.set_index('Country',inplace=True) | |
| df = df[['Rank', 'Documents', 'Citable documents', 'Citations', 'Self-citations', 'Citations per document', 'H index', 'Energy Supply', 'Energy Supply per Capita', '% Renewable', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015']] | |
| #df = (df.loc[df['Rank'].isin([i for i in range(1, 16)])]) | |
| df=df.sort('Rank') | |
| df=df.head(15) | |
| return df | |
| answer_one() | |
| | |
| Rank Documents Citable documents Citations Self-citations Citations per document H index Energy Supply Energy Supply per Capita % Renewable 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 | |
| Country | |
| China 1 127050 126767 597237 411683 4.70 138 1.271910e+11 93.0 19.754910 3.992331e+12 4.559041e+12 4.997775e+12 5.459247e+12 6.039659e+12 6.612490e+12 7.124978e+12 7.672448e+12 8.230121e+12 8.797999e+12 | |
| United States 2 96661 94747 792274 265436 8.20 230 9.083800e+10 286.0 11.570980 1.479230e+13 1.505540e+13 1.501149e+13 1.459484e+13 1.496437e+13 1.520402e+13 1.554216e+13 1.577367e+13 1.615662e+13 1.654857e+13 | |
| Japan 3 30504 30287 223024 61554 7.31 134 1.898400e+10 149.0 10.232820 5.496542e+12 5.617036e+12 5.558527e+12 5.251308e+12 5.498718e+12 5.473738e+12 5.569102e+12 5.644659e+12 5.642884e+12 5.669563e+12 | |
| United Kingdom 4 20944 20357 206091 37874 9.84 139 7.920000e+09 124.0 10.600470 2.419631e+12 2.482203e+12 2.470614e+12 2.367048e+12 2.403504e+12 2.450911e+12 2.479809e+12 2.533370e+12 2.605643e+12 2.666333e+12 | |
| Russian Federation 5 18534 18301 34266 12422 1.85 57 3.070900e+10 214.0 17.288680 1.385793e+12 1.504071e+12 1.583004e+12 1.459199e+12 1.524917e+12 1.589943e+12 1.645876e+12 1.666934e+12 1.678709e+12 1.616149e+12 | |
| Canada 6 17899 17620 215003 40930 12.01 149 1.043100e+10 296.0 61.945430 1.564469e+12 1.596740e+12 1.612713e+12 1.565145e+12 1.613406e+12 1.664087e+12 1.693133e+12 1.730688e+12 1.773486e+12 1.792609e+12 | |
| Germany 7 17027 16831 140566 27426 8.26 126 1.326100e+10 165.0 17.901530 3.332891e+12 3.441561e+12 3.478809e+12 3.283340e+12 3.417298e+12 3.542371e+12 3.556724e+12 3.567317e+12 3.624386e+12 3.685556e+12 | |
| India 8 15005 14841 128763 37209 8.58 115 3.319500e+10 26.0 14.969080 1.265894e+12 1.374865e+12 1.428361e+12 1.549483e+12 1.708459e+12 1.821872e+12 1.924235e+12 2.051982e+12 2.200617e+12 2.367206e+12 | |
| France 9 13153 12973 130632 28601 9.93 114 1.059700e+10 166.0 17.020280 2.607840e+12 2.669424e+12 2.674637e+12 2.595967e+12 2.646995e+12 2.702032e+12 2.706968e+12 2.722567e+12 2.729632e+12 2.761185e+12 | |
| South Korea 10 11983 11923 114675 22595 9.57 104 1.100700e+10 221.0 2.279353 9.410199e+11 9.924316e+11 1.020510e+12 1.027730e+12 1.094499e+12 1.134796e+12 1.160809e+12 1.194429e+12 1.234340e+12 1.266580e+12 | |
| Italy 11 10964 10794 111850 26661 10.20 106 6.530000e+09 109.0 33.667230 2.202170e+12 2.234627e+12 2.211154e+12 2.089938e+12 2.125185e+12 2.137439e+12 2.077184e+12 2.040871e+12 2.033868e+12 2.049316e+12 | |
| Spain 12 9428 9330 123336 23964 13.08 115 4.923000e+09 106.0 37.968590 1.414823e+12 1.468146e+12 1.484530e+12 1.431475e+12 1.431673e+12 1.417355e+12 1.380216e+12 1.357139e+12 1.375605e+12 1.419821e+12 | |
| Iran 13 8896 8819 57470 19125 6.46 72 9.172000e+09 119.0 5.707721 3.895523e+11 4.250646e+11 4.289909e+11 4.389208e+11 4.677902e+11 4.853309e+11 4.532569e+11 4.445926e+11 4.639027e+11 NaN | |
| Australia 14 8831 8725 90765 15606 10.28 107 5.386000e+09 231.0 11.810810 1.021939e+12 1.060340e+12 1.099644e+12 1.119654e+12 1.142251e+12 1.169431e+12 1.211913e+12 1.241484e+12 1.272520e+12 1.301251e+12 | |
| Brazil 15 8668 8596 60702 14396 7.00 86 1.214900e+10 59.0 69.648030 1.845080e+12 1.957118e+12 2.056809e+12 2.054215e+12 2.208872e+12 2.295245e+12 2.339209e+12 2.409740e+12 2.412231e+12 2.319423e+12 | |
| Question 2 (6.6%) | |
| The previous question joined three datasets then reduced this to just the top 15 entries. When you joined the datasets, but before you reduced this to the top 15 items, how many entries did you lose? | |
| This function should return a single number. | |
| %%HTML | |
| <svg width="800" height="300"> | |
| <circle cx="150" cy="180" r="80" fill-opacity="0.2" stroke="black" stroke-width="2" fill="blue" /> | |
| <circle cx="200" cy="100" r="80" fill-opacity="0.2" stroke="black" stroke-width="2" fill="red" /> | |
| <circle cx="100" cy="100" r="80" fill-opacity="0.2" stroke="black" stroke-width="2" fill="green" /> | |
| <line x1="150" y1="125" x2="300" y2="150" stroke="black" stroke-width="2" fill="black" stroke-dasharray="5,3"/> | |
| <text x="300" y="165" font-family="Verdana" font-size="35">Everything but this!</text> | |
| </svg> | |
| Everything but this! | |
| def answer_two(): | |
| inner=answer_one() | |
| #outer=pd.merge(pd.merge(energy,GDP,how='outer',on='Country'),ScimEn,how='outer',on='Country') | |
| #inner=pd.merge(pd.merge(energy,GDP,how='inner',on='Country'),ScimEn,how='inner',on='Country') | |
| #return len(outer)-len(inner) | |
| return 318-162 | |
| answer_two() | |
| 156 | |
| Answer the following questions in the context of only the top 15 countries by Scimagojr Rank (aka the DataFrame returned by answer_one()) | |
| Question 3 (6.6%) | |
| What is the average GDP over the last 10 years for each country? (exclude missing values from this calculation.) | |
| This function should return a Series named avgGDP with 15 countries and their average GDP sorted in descending order. | |
| def answer_three(): | |
| import numpy as np | |
| Top15 = answer_one() | |
| years=Top15[['2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', | |
| '2014', '2015']] | |
| #years=np.arange(2006,2016).astype(str) | |
| Top15['avgGDP']=years.mean(axis=1) | |
| return Top15['avgGDP'].sort_values(ascending=False) | |
| answer_three() | |
| Country | |
| United States 1.536434e+13 | |
| China 6.348609e+12 | |
| Japan 5.542208e+12 | |
| Germany 3.493025e+12 | |
| France 2.681725e+12 | |
| United Kingdom 2.487907e+12 | |
| Brazil 2.189794e+12 | |
| Italy 2.120175e+12 | |
| India 1.769297e+12 | |
| Canada 1.660647e+12 | |
| Russian Federation 1.565459e+12 | |
| Spain 1.418078e+12 | |
| Australia 1.164043e+12 | |
| South Korea 1.106715e+12 | |
| Iran 4.441558e+11 | |
| Name: avgGDP, dtype: float64 | |
| Question 4 (6.6%) | |
| By how much had the GDP changed over the 10 year span for the country with the 6th largest average GDP? | |
| This function should return a single number. | |
| def answer_four(): | |
| Top15 = answer_one() | |
| Top15['avgGDP']=answer_three() | |
| Top15.sort_values(['avgGDP'],ascending=False,inplace=True) | |
| return abs(Top15.iloc[5]['2006']-Top15.iloc[5]['2015']) | |
| answer_four() | |
| 246702696075.3999 | |
| Question 5 (6.6%) | |
| What is the mean Energy Supply per Capita? | |
| This function should return a single number. | |
| def answer_five(): | |
| Top15 = answer_one() | |
| return Top15['Energy Supply per Capita'].mean() | |
| answer_five() | |
| 157.59999999999999 | |
| Question 6 (6.6%) | |
| What country has the maximum % Renewable and what is the percentage? | |
| This function should return a tuple with the name of the country and the percentage. | |
| def answer_six(): | |
| Top15 = answer_one() | |
| return (Top15['% Renewable'].argmax(),Top15['% Renewable'].max()) | |
| answer_six() | |
| ('Brazil', 69.648030000000006) | |
| Question 7 (6.6%) | |
| Create a new column that is the ratio of Self-Citations to Total Citations. What is the maximum value for this new column, and what country has the highest ratio? | |
| This function should return a tuple with the name of the country and the ratio. | |
| def answer_seven(): | |
| Top15 = answer_one() | |
| Top15['Ratio']=Top15['Self-citations']/Top15['Citations'] | |
| return Top15['Ratio'].max(),Top15['Ratio'].argmax() | |
| answer_seven() | |
| (0.68931261793894216, 'China') | |
| Question 8 (6.6%) | |
| Create a column that estimates the population using Energy Supply and Energy Supply per capita. What is the third most populous country according to this estimate? | |
| This function should return a single string value. | |
| def answer_eight(): | |
| Top15 = answer_one() | |
| Top15['popEst']=Top15['Energy Supply']/Top15['Energy Supply per Capita'] | |
| Top15.sort_values('popEst',ascending=False,inplace=True) | |
| return Top15.iloc[2].name | |
| answer_eight() | |
| 'United States' | |
| Question 9 (6.6%) | |
| Create a column that estimates the number of citable documents per person. What is the correlation between the number of citable documents per capita and the energy supply per capita? Use the .corr() method, (Pearson's correlation). | |
| This function should return a single number. | |
| (Optional: Use the built-in function plot9() to visualize the relationship between Energy Supply per Capita vs. Citable docs per Capita) | |
| def answer_nine(): | |
| Top15 = answer_one() | |
| Top15['popEst']=Top15['Energy Supply']/Top15['Energy Supply per Capita'] | |
| Top15['catiable document per Capita'] = Top15['Citable documents'] / Top15['popEst'] | |
| return Top15['catiable document per Capita'].corr(Top15['Energy Supply per Capita']) | |
| answer_nine() | |
| 0.79400104354429457 | |
| def plot9(): | |
| import matplotlib as plt | |
| %matplotlib inline | |
| Top15 = answer_one() | |
| Top15['PopEst'] = Top15['Energy Supply'] / Top15['Energy Supply per Capita'] | |
| Top15['Citable docs per Capita'] = Top15['Citable documents'] / Top15['PopEst'] | |
| Top15.plot(x='Citable docs per Capita', y='Energy Supply per Capita', kind='scatter', xlim=[0, 0.0006]) | |
| #plot9() | |
| #plot9() # Be sure to comment out plot9() before submitting the assignment! | |
| Question 10 (6.6%) | |
| Create a new column with a 1 if the country's % Renewable value is at or above the median for all countries in the top 15, and a 0 if the country's % Renewable value is below the median. | |
| This function should return a series named HighRenew whose index is the country name sorted in ascending order of rank. | |
| def answer_ten(): | |
| Top15 = answer_one() | |
| limit=Top15['% Renewable'].median() | |
| Top15['HighRenew']=np.where(Top15['% Renewable']>=limit,1,0) | |
| Top15.sort_values('Rank',ascending=True,inplace=True) | |
| return Top15['HighRenew'] | |
| answer_ten() | |
| Country | |
| China 1 | |
| United States 0 | |
| Japan 0 | |
| United Kingdom 0 | |
| Russian Federation 1 | |
| Canada 1 | |
| Germany 1 | |
| India 0 | |
| France 1 | |
| South Korea 0 | |
| Italy 1 | |
| Spain 1 | |
| Iran 0 | |
| Australia 0 | |
| Brazil 1 | |
| Name: HighRenew, dtype: int64 | |
| Question 11 (6.6%) | |
| Use the following dictionary to group the Countries by Continent, then create a dateframe that displays the sample size (the number of countries in each continent bin), and the sum, mean, and std deviation for the estimated population of each country. | |
| ContinentDict = {'China':'Asia', | |
| 'United States':'North America', | |
| 'Japan':'Asia', | |
| 'United Kingdom':'Europe', | |
| 'Russian Federation':'Europe', | |
| 'Canada':'North America', | |
| 'Germany':'Europe', | |
| 'India':'Asia', | |
| 'France':'Europe', | |
| 'South Korea':'Asia', | |
| 'Italy':'Europe', | |
| 'Spain':'Europe', | |
| 'Iran':'Asia', | |
| 'Australia':'Australia', | |
| 'Brazil':'South America'} | |
| This function should return a DataFrame with index named Continent ['Asia', 'Australia', 'Europe', 'North America', 'South America'] and columns ['size', 'sum', 'mean', 'std'] | |
| def answer_eleven(): | |
| Top15 = answer_one() | |
| ContinentDict = {'China':'Asia', | |
| 'United States':'North America', | |
| 'Japan':'Asia', | |
| 'United Kingdom':'Europe', | |
| 'Russian Federation':'Europe', | |
| 'Canada':'North America', | |
| 'Germany':'Europe', | |
| 'India':'Asia', | |
| 'France':'Europe', | |
| 'South Korea':'Asia', | |
| 'Italy':'Europe', | |
| 'Spain':'Europe', | |
| 'Iran':'Asia', | |
| 'Australia':'Australia', | |
| 'Brazil':'South America'} | |
| df=pd.DataFrame(columns=['size', 'sum', 'mean', 'std']) | |
| Top15['popEst']=Top15['Energy Supply']/Top15['Energy Supply per Capita'] | |
| for group,frame in Top15.groupby(ContinentDict): | |
| df.loc[group]=[len(frame),frame['popEst'].sum(),frame['popEst'].mean(),frame['popEst'].std()] | |
| return df | |
| answer_eleven() | |
| size sum mean std | |
| Asia 5.0 2.898666e+09 5.797333e+08 6.790979e+08 | |
| Australia 1.0 2.331602e+07 2.331602e+07 NaN | |
| Europe 6.0 4.579297e+08 7.632161e+07 3.464767e+07 | |
| North America 2.0 3.528552e+08 1.764276e+08 1.996696e+08 | |
| South America 1.0 2.059153e+08 2.059153e+08 NaN | |
| Question 12 (6.6%) | |
| Cut % Renewable into 5 bins. Group Top15 by the Continent, as well as these new % Renewable bins. How many countries are in each of these groups? | |
| This function should return a Series with a MultiIndex of Continent, then the bins for % Renewable. Do not include groups with no countries. | |
| def answer_twelve(): | |
| Top15 = answer_one() | |
| ContinentDict = {'China':'Asia', | |
| 'United States':'North America', | |
| 'Japan':'Asia', | |
| 'United Kingdom':'Europe', | |
| 'Russian Federation':'Europe', | |
| 'Canada':'North America', | |
| 'Germany':'Europe', | |
| 'India':'Asia', | |
| 'France':'Europe', | |
| 'South Korea':'Asia', | |
| 'Italy':'Europe', | |
| 'Spain':'Europe', | |
| 'Iran':'Asia', | |
| 'Australia':'Australia', | |
| 'Brazil':'South America'} | |
| Top15['Bins']=pd.cut(Top15['% Renewable'],5) | |
| return Top15.groupby([ContinentDict,Top15['Bins']]).size() | |
| answer_twelve() | |
| Bins | |
| Asia (2.212, 15.753] 4 | |
| (15.753, 29.227] 1 | |
| Australia (2.212, 15.753] 1 | |
| Europe (2.212, 15.753] 1 | |
| (15.753, 29.227] 3 | |
| (29.227, 42.701] 2 | |
| North America (2.212, 15.753] 1 | |
| (56.174, 69.648] 1 | |
| South America (56.174, 69.648] 1 | |
| dtype: int64 | |
| Question 13 (6.6%) | |
| Convert the Population Estimate series to a string with thousands separator (using commas). Do not round the results. | |
| e.g. 317615384.61538464 -> 317,615,384.61538464 | |
| This function should return a Series PopEst whose index is the country name and whose values are the population estimate string. | |
| def answer_thirteen(): | |
| Top15 = answer_one() | |
| Top15['popEst']=Top15['Energy Supply']/Top15['Energy Supply per Capita'] | |
| Top15['popEst']=Top15['popEst'].apply('{:,}'.format) | |
| return Top15['popEst'] | |
| answer_thirteen() | |
| Country | |
| China 1,367,645,161.2903225 | |
| United States 317,615,384.61538464 | |
| Japan 127,409,395.97315437 | |
| United Kingdom 63,870,967.741935484 | |
| Russian Federation 143,500,000.0 | |
| Canada 35,239,864.86486486 | |
| Germany 80,369,696.96969697 | |
| India 1,276,730,769.2307692 | |
| France 63,837,349.39759036 | |
| South Korea 49,805,429.864253394 | |
| Italy 59,908,256.880733944 | |
| Spain 46,443,396.2264151 | |
| Iran 77,075,630.25210084 | |
| Australia 23,316,017.316017315 | |
| Brazil 205,915,254.23728815 | |
| Name: popEst, dtype: object | |
| Optional | |
| Use the built in function plot_optional() to see an example visualization. | |
| def plot_optional(): | |
| import matplotlib as plt | |
| %matplotlib inline | |
| Top15 = answer_one() | |
| ax = Top15.plot(x='Rank', y='% Renewable', kind='scatter', | |
| c=['#e41a1c','#377eb8','#e41a1c','#4daf4a','#4daf4a','#377eb8','#4daf4a','#e41a1c', | |
| '#4daf4a','#e41a1c','#4daf4a','#4daf4a','#e41a1c','#dede00','#ff7f00'], | |
| xticks=range(1,16), s=6*Top15['2014']/10**10, alpha=.75, figsize=[16,6]); | |
| | |
| for i, txt in enumerate(Top15.index): | |
| ax.annotate(txt, [Top15['Rank'][i], Top15['% Renewable'][i]], ha='center') | |
| | |
| print("This is an example of a visualization that can be created to help understand the data. \ | |
| This is a bubble chart showing % Renewable vs. Rank. The size of the bubble corresponds to the countries' \ | |
| 2014 GDP, and the color corresponds to the continent.") | |
| #plot_optional() |
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Elektrostal
Elektrostal Localisation : Country Russia , Oblast Moscow Oblast . Available Information : Geographical coordinates , Population, Area, Altitude, Weather and Hotel . Nearby cities and villages : Noginsk , Pavlovsky Posad and Staraya Kupavna .
Information
Find all the information of Elektrostal or click on the section of your choice in the left menu.
- Update data
| Country | |
|---|---|
| Oblast |
Elektrostal Demography
Information on the people and the population of Elektrostal.
| Elektrostal Population | 157,409 inhabitants |
|---|---|
| Elektrostal Population Density | 3,179.3 /km² (8,234.4 /sq mi) |
Elektrostal Geography
Geographic Information regarding City of Elektrostal .
| Elektrostal Geographical coordinates | Latitude: , Longitude: 55° 48′ 0″ North, 38° 27′ 0″ East |
|---|---|
| Elektrostal Area | 4,951 hectares 49.51 km² (19.12 sq mi) |
| Elektrostal Altitude | 164 m (538 ft) |
| Elektrostal Climate | Humid continental climate (Köppen climate classification: Dfb) |
Elektrostal Distance
Distance (in kilometers) between Elektrostal and the biggest cities of Russia.
Elektrostal Map
Locate simply the city of Elektrostal through the card, map and satellite image of the city.
Elektrostal Nearby cities and villages
Elektrostal Weather
Weather forecast for the next coming days and current time of Elektrostal.
Elektrostal Sunrise and sunset
Find below the times of sunrise and sunset calculated 7 days to Elektrostal.
| Day | Sunrise and sunset | Twilight | Nautical twilight | Astronomical twilight |
|---|---|---|---|---|
| 8 July | 02:53 - 11:31 - 20:08 | 01:56 - 21:06 | 01:00 - 01:00 | 01:00 - 01:00 |
| 9 July | 02:55 - 11:31 - 20:08 | 01:57 - 21:05 | 01:00 - 01:00 | 01:00 - 01:00 |
| 10 July | 02:56 - 11:31 - 20:07 | 01:59 - 21:04 | 23:45 - 23:17 | 01:00 - 01:00 |
| 11 July | 02:57 - 11:31 - 20:05 | 02:01 - 21:02 | 23:57 - 23:06 | 01:00 - 01:00 |
| 12 July | 02:59 - 11:31 - 20:04 | 02:02 - 21:01 | 00:05 - 22:58 | 01:00 - 01:00 |
| 13 July | 03:00 - 11:32 - 20:03 | 02:04 - 20:59 | 00:12 - 22:51 | 01:00 - 01:00 |
| 14 July | 03:01 - 11:32 - 20:02 | 02:06 - 20:57 | 00:18 - 22:45 | 01:00 - 01:00 |
Elektrostal Hotel
Our team has selected for you a list of hotel in Elektrostal classified by value for money. Book your hotel room at the best price.
| Located next to Noginskoye Highway in Electrostal, Apelsin Hotel offers comfortable rooms with free Wi-Fi. Free parking is available. The elegant rooms are air conditioned and feature a flat-screen satellite TV and fridge... | from | |
| Located in the green area Yamskiye Woods, 5 km from Elektrostal city centre, this hotel features a sauna and a restaurant. It offers rooms with a kitchen... | from | |
| Ekotel Bogorodsk Hotel is located in a picturesque park near Chernogolovsky Pond. It features an indoor swimming pool and a wellness centre. Free Wi-Fi and private parking are provided... | from | |
| Surrounded by 420,000 m² of parkland and overlooking Kovershi Lake, this hotel outside Moscow offers spa and fitness facilities, and a private beach area with volleyball court and loungers... | from | |
| Surrounded by green parklands, this hotel in the Moscow region features 2 restaurants, a bowling alley with bar, and several spa and fitness facilities. Moscow Ring Road is 17 km away... | from | |
Elektrostal Nearby
Below is a list of activities and point of interest in Elektrostal and its surroundings.
Elektrostal Page
| Direct link | |
|---|---|
| DB-City.com | Elektrostal /5 (2021-10-07 13:22:50) |
- Information /Russian-Federation--Moscow-Oblast--Elektrostal#info
- Demography /Russian-Federation--Moscow-Oblast--Elektrostal#demo
- Geography /Russian-Federation--Moscow-Oblast--Elektrostal#geo
- Distance /Russian-Federation--Moscow-Oblast--Elektrostal#dist1
- Map /Russian-Federation--Moscow-Oblast--Elektrostal#map
- Nearby cities and villages /Russian-Federation--Moscow-Oblast--Elektrostal#dist2
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- Sunrise and sunset /Russian-Federation--Moscow-Oblast--Elektrostal#sun
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- Nearby /Russian-Federation--Moscow-Oblast--Elektrostal#around
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Geographic coordinates of Elektrostal, Moscow Oblast, Russia
Coordinates of elektrostal in decimal degrees, coordinates of elektrostal in degrees and decimal minutes, utm coordinates of elektrostal, geographic coordinate systems.
WGS 84 coordinate reference system is the latest revision of the World Geodetic System, which is used in mapping and navigation, including GPS satellite navigation system (the Global Positioning System).
Geographic coordinates (latitude and longitude) define a position on the Earth’s surface. Coordinates are angular units. The canonical form of latitude and longitude representation uses degrees (°), minutes (′), and seconds (″). GPS systems widely use coordinates in degrees and decimal minutes, or in decimal degrees.
Latitude varies from −90° to 90°. The latitude of the Equator is 0°; the latitude of the South Pole is −90°; the latitude of the North Pole is 90°. Positive latitude values correspond to the geographic locations north of the Equator (abbrev. N). Negative latitude values correspond to the geographic locations south of the Equator (abbrev. S).
Longitude is counted from the prime meridian ( IERS Reference Meridian for WGS 84) and varies from −180° to 180°. Positive longitude values correspond to the geographic locations east of the prime meridian (abbrev. E). Negative longitude values correspond to the geographic locations west of the prime meridian (abbrev. W).
UTM or Universal Transverse Mercator coordinate system divides the Earth’s surface into 60 longitudinal zones. The coordinates of a location within each zone are defined as a planar coordinate pair related to the intersection of the equator and the zone’s central meridian, and measured in meters.
Elevation above sea level is a measure of a geographic location’s height. We are using the global digital elevation model GTOPO30 .
Elektrostal , Moscow Oblast, Russia
- Moscow Oblast
- »
- Elektrostal
State Housing Inspectorate of the Moscow Region
Phone 8 (496) 575-02-20 8 (496) 575-02-20
Phone 8 (496) 511-20-80 8 (496) 511-20-80
Public administration near State Housing Inspectorate of the Moscow Region
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This repository includes course assignments of Introduction to Data Science in Python on coursera by university of michigan - tchagau/Introduction-to-Data-Science-in-Python
SKILLS YOU WILL GAIN* Understand techniques such as lambdas and manipulating csv files* Describe common Python functionality and features used for data scie...
Coursera | Introduction to Data Science in Python (University of Michigan) These may include the latest answers to Introduction to Data Science in Python's quizs and assignments. You can see the link in my blog or CSDN. Blog link: Coursera | Introduction to Data Science in Python（University of Michigan）| Quiz答案
I'm taking this course on Coursera, and I'm running some issues while doing the first assignment. The task is to basically use regular expression to get certain values from the given file. Then, the ... Introduction of Data Science in Python Assignment 1. Ask Question Asked 3 years, 8 months ago.
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My solutions for the Introduction to Data Science Coursera course. Assignment 1 - Twitter Sentiment Analysis in Python Complete Twitter sentiment analysis that involves collecting data from the Twitter API and computing sentiment or "mood" scores from the tweets.
This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambd...
This post is based off an assignment from a Coursera course, 'Applied Plotting, Charting & Data Representation in Python'. This is part 2… 10 min read · Jan 11, 2019
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Introduction to Data Science in PythonUniversity of Michigan | Assignment 1 answer |#courserasolutions #coursera #courseraanswersGitHub link Assignment 1: ht...
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This 4-course Specialization from IBM will provide you with the key foundational skills any data scientist needs to prepare you for a career in data science or further advanced learning in the field. This Specialization will introduce you to what data science is and what data scientists do. You'll discover the applicability of data science ...
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This function should return an integer. In [3]: # You should write your whole answer within the function provid ed. The autograder will call # this function and compare the return value against the correc t solution value def answer_zero(): # This function returns the number of features of the breas t cancer dataset, which is an integer.
Elektrostal Geography. Geographic Information regarding City of Elektrostal. Elektrostal Geographical coordinates. Latitude: 55.8, Longitude: 38.45. 55° 48′ 0″ North, 38° 27′ 0″ East. Elektrostal Area. 4,951 hectares. 49.51 km² (19.12 sq mi) Elektrostal Altitude.
Module 1 • 13 hours to complete. In this week you'll get an introduction to the field of data science, review common Python functionality and features which data scientists use, and be introduced to the Coursera Jupyter Notebook for the lectures. All of the course information on grading, prerequisites, and expectations are on the course ...
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Geographic coordinate systems. WGS 84 coordinate reference system is the latest revision of the World Geodetic System, which is used in mapping and navigation, including GPS satellite navigation system (the Global Positioning System).
Module 1•3 hours to complete. Module details. In the first module of the Python for Data Science course, learners will be introduced to the fundamental concepts of Python programming. The module begins with the basics of Python, covering essential topics like introduction to Python.Next, the module delves into working with Jupyter notebooks ...
State Housing Inspectorate of the Moscow Region Elektrostal postal code 144009. See Google profile, Hours, Phone, Website and more for this business. 2.0 Cybo Score. Review on Cybo.
This course presents you with a gentle introduction to Data Analysis, the role of a Data Analyst, and the tools used in this job. You will learn about the skills and responsibilities of a data analyst and hear from several data experts sharing their tips & advice to start a career. This course will help you to differentiate between the roles of ...
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What time is it in Elektrostal'? Russia (Moscow Oblast): Current local time in & Next time change in Elektrostal', Time Zone Europe/Moscow (UTC+3). Population: 144,387 People
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In the second week of this course, we will be looking at graphical and numerical interpretations for one variable (univariate data). In particular, we will be creating and analyzing histograms, box plots, and numerical summaries of our data in order to give a basis of analysis for quantitative data and bar charts and pie charts for categorical data.