csaid

# 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) %>%

csaid

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))
)

csaid

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))

csaid

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):

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