data-science

问题 I am trying to accomplish something similar to what is here : https://www.opportunityatlas.org/. If you proceed further to this link and click on 'Show Distribution' to see the graph and select 'On Screen' and then move the cursor around the map you will see the size of the rectangles changes and also the update patterns works i.e. if a rectangle was already there it moves horizontally to the new value. I have tried doing the same but could not achieve the update part . Could you please point

问题 I have dataframe total_year , which contains three columns ( year , action , comedy ) . total_year I want to plot the year column on the x-axis, and action & comedy both on the y-axis. How can I plot two columns ( action and comedy ) on y-axis? My code plots only one column on y-axis. total_year[-15:].plot(x='year', y='action', figsize=(10,5), grid=True) 回答1: Pandas.DataFrame.plot() per default uses index for plotting X axis, all other numeric columns will be used as Y values. So setting year

问题 I have dataframe total_year , which contains three columns ( year , action , comedy ) . total_year I want to plot the year column on the x-axis, and action & comedy both on the y-axis. How can I plot two columns ( action and comedy ) on y-axis? My code plots only one column on y-axis. total_year[-15:].plot(x='year', y='action', figsize=(10,5), grid=True) 回答1: Pandas.DataFrame.plot() per default uses index for plotting X axis, all other numeric columns will be used as Y values. So setting year

问题 I have dataframe total_year , which contains three columns ( year , action , comedy ) . total_year I want to plot the year column on the x-axis, and action & comedy both on the y-axis. How can I plot two columns ( action and comedy ) on y-axis? My code plots only one column on y-axis. total_year[-15:].plot(x='year', y='action', figsize=(10,5), grid=True) 回答1: Pandas.DataFrame.plot() per default uses index for plotting X axis, all other numeric columns will be used as Y values. So setting year

问题 I've tried creating an ML model to make some predictions, but I keep running into a stumbling block. Namely, the code seems to be ignoring the imputation instructions I give it, resulting in the following error: ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). Here's my code: import pandas as pd import numpy as np from sklearn.ensemble import AdaBoostRegressor from category_encoders import CatBoostEncoder from sklearn.compose import make_column_transformer

问题 I've tried creating an ML model to make some predictions, but I keep running into a stumbling block. Namely, the code seems to be ignoring the imputation instructions I give it, resulting in the following error: ValueError: Input contains NaN, infinity or a value too large for dtype('float64'). Here's my code: import pandas as pd import numpy as np from sklearn.ensemble import AdaBoostRegressor from category_encoders import CatBoostEncoder from sklearn.compose import make_column_transformer

问题 I have some data of time (hours of the day). I would like to fit a von mises distribution to this data, and find the periodic mean. How do I do this using scipy in python ? for example : from scipy.stats import vonmises data = [1, 2, 22, 23] A = vonmises.fit(data) I am not sure how do I get the distribution (interval probably) and periodic mean of this data using fit or mean or interval methods. 回答1: Good job on finding the VM distribution. That's half of the battle. But unless I'm mistaken

问题 I have some data of time (hours of the day). I would like to fit a von mises distribution to this data, and find the periodic mean. How do I do this using scipy in python ? for example : from scipy.stats import vonmises data = [1, 2, 22, 23] A = vonmises.fit(data) I am not sure how do I get the distribution (interval probably) and periodic mean of this data using fit or mean or interval methods. 回答1: Good job on finding the VM distribution. That's half of the battle. But unless I'm mistaken

问题 What is the difference between Kernel Ridge (from sklearn.kernel_ridge) with polynomial kernel and using PolynomialFeatures + Ridge (from sklearn.linear_model)? 回答1: The difference is in feature computation. PolynomialFeatures explicitly computes polynomial combinations between the input features up to the desired degree while KernelRidge(kernel='poly') only considers a polynomial kernel (a polynomial representation of feature dot products) which will be expressed in terms of the original

问题 By default the seaborn displaces the X axis ranges from -5 to 35 in distplots. But I need to display the distplots with the X axis ranges from 1 to 30 with 1 unit. How can I do that? 回答1: For the most flexible control with these kind of plots, create your own axes object then add the seaborn plots to it. Then you can perform the standard matplotlib changes to features like the x-axis, or use any of the normal controls available through the matplotlib API. import matplotlib.pyplot as plt