pandas

问题 Country life_expectancy population Germany 70 3000000 France 75 450000 USA 70 350000 India 65 4000000 Pakistan 60 560000 Belgium 68 230000 I want to calculate the weighted average life expectancy according to the formula below: ∑ (𝑙𝑖𝑓𝑒𝑖 × 𝑝𝑜𝑝𝑖)/ ∑ 𝑝𝑜𝑝𝑖 where 𝑙𝑖𝑓𝑒𝑖 = life expectancy 𝑝𝑜𝑝𝑖 = population NOTE: The weighted average life expectancy is computed with the sum of the products of life expectancy by the total population of each country divided by the sum of the total population of each

问题 I have created a holoviews chord plot where each node is associated with particular group(community). I have a requirement that whenever I tap on a particular node all the edge associated with it should be same color as the color of the node. For example in this chord diagram example chord If I select a blue node the edge color should be same as node color(i.e blue). But Here when I click on a blue node I am getting few edges of of blue and few edges of orange color(basically the edge takes

问题 Following up this question and dataframes, I am trying to convert a dataframe into a dictionary. In pandas I was using this: dictionary = df_2.unstack().to_dict(orient='index') However, I need to convert this code to pyspark. Can anyone help me with this? As I understand from previous questions such as this I would indeed need to use pandas, but the dataframe is way too big for me to be able to do this. How can I solve this? EDIT: I have now tried the following approach: dictionary_list = map

问题 Following up this question and dataframes, I am trying to convert a dataframe into a dictionary. In pandas I was using this: dictionary = df_2.unstack().to_dict(orient='index') However, I need to convert this code to pyspark. Can anyone help me with this? As I understand from previous questions such as this I would indeed need to use pandas, but the dataframe is way too big for me to be able to do this. How can I solve this? EDIT: I have now tried the following approach: dictionary_list = map

问题 I need to research something about what are the most cost efficient movie genres. My problem is that the genres are provided all within one string: This gives me about 300 different unique categories. How can I split these into about 12 original dummy genre columns so I can analyse each main genre? 回答1: Thanks to Yong Wang who suggested the get_dummies function within pandas. We can shorten the code significantly: df = pd.DataFrame({ 'movie_id': range(5), 'gernes': [ 'Action|Adventure|Fantasy

问题 I need to research something about what are the most cost efficient movie genres. My problem is that the genres are provided all within one string: This gives me about 300 different unique categories. How can I split these into about 12 original dummy genre columns so I can analyse each main genre? 回答1: Thanks to Yong Wang who suggested the get_dummies function within pandas. We can shorten the code significantly: df = pd.DataFrame({ 'movie_id': range(5), 'gernes': [ 'Action|Adventure|Fantasy

问题 I need to compare two columns together: "EMAIL" and "LOCATION". I'm using Email because it's more accurate than name for this issue. My objective is to find total number of locations each person worked at, sum up the total of locations to select which sheet the data will been written to and copy the original data over to the new sheet(tab). I need the original data copied over with all the duplicate locations, which is where this problem stumps me. Full Excel Sheet Had to use images because

问题 I need to compare two columns together: "EMAIL" and "LOCATION". I'm using Email because it's more accurate than name for this issue. My objective is to find total number of locations each person worked at, sum up the total of locations to select which sheet the data will been written to and copy the original data over to the new sheet(tab). I need the original data copied over with all the duplicate locations, which is where this problem stumps me. Full Excel Sheet Had to use images because

问题 Imagine I have a very big csv file with 500 rows and 500 columns. Part of the data shown : a small section of my data I cannot delete the first couple of rows from my file but I can omit them using "skiprows" while reading the file. Then I want to plot my data and all methods that I try fail. I actually get a plot if I just use 'plot()" command but what I want is to have the first column as my x data and the rest of 499 columns as my y data. Could you please help me with this? 回答1: If df is

问题 I have (in my opinion) a strange problem with python pandas. If I do: cc1 = cc.copy(deep=True) for the dataframe cc and than ask a certain row and column: print(cc1.loc['myindex']['data'] is cc.loc['myindex']['data']) I get True What's wrong here? 回答1: Deep copying doesn't work in pandas and the devs consider putting mutable objects inside a DataFrame as an antipattern There is nothing wrong in your code, just in case if you want to know the difference with some example of deep and shallow