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@csaid
csaid / shower_problem_tau_samples.csv
Last active Feb 9, 2020
20,000 shower samples that can be used to solve the shower problem described in https://chris-said.io/2020/02/08/the-shower-problem/. Direction (left of right) is sampled from Bernoulli trials. Tau is sampled from a two-parameter Weibull distribution, using scipy's weibull_min: weibull_min.rvs(1.5, loc=0, scale=50, size=20_000)
View shower_problem_tau_samples.csv
direction tau
1.0 44.17309441579963
1.0 39.13313136222197
1.0 2.229445925263579
1.0 45.51885740055968
1.0 32.46369490822559
0.0 34.32693114812909
1.0 29.851265008411353
1.0 22.108872716835183
0.0 39.32300084317585
View econ_survey.py
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import cps
import seaborn as sns
sns.set_style('white')
plt.ion()
# This is a quick hack that gets the job done :/
def repulsion(arr):
View dplython
from dplython import *
diamonds >> group_by(X.cut) >> summarize(mean_x = np.mean(X.x), var_y = np.var(X.y))
diamonds >> group_by(X.cut) >> mutate(mean_x = np.mean(X.x), var_y = np.var(X.y))
View diamonds.R
# R
# 1) Easily create multiple summaries of multiple column.
diamonds %>%
group_by(cut) %>%
summarize(mean_x = mean(x), var_xy = var(x * y))
# 2) Easily put multiple summaries back into the original data frame
diamonds %>%
group_by(cut) %>%
View diamonds.py
import pandas as pd
from ggplot import diamonds
# 1) How do I easily create multiple summaries of multiple columns?
(
diamonds
.groupby('cut')
.agg(??) # Equivalent of summarize(mean_x = mean(x), var_xy = var(xy))
)
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