Keiku

import pandas as pd

df1 = pd.DataFrame({'id': [1, 2, 3]})
df2 = pd.DataFrame({'id': [2, 3, 4]})
set(df1.id).intersection(set(df2.id))
# Out[73]: {2, 3}

Keiku

import pandas as pd
from sklearn import datasets
iris = datasets.load_iris()

iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)
iris_df['species'] = iris.target
mapping = {0 : 'setosa', 1: 'versicolor', 2: 'virginica'}
iris_df = iris_df.replace({'species': mapping})

iris_df['sepal length (bins)'] = pd.cut(iris_df['sepal length (cm)'], bins=[0, 3, 6, 9], include_lowest=False, right=True)

Keiku

library(stringr)

add_backquotes <- function(x) paste0("`", x, "`")
add_doublequotes <- function(x) paste0("\"", x, "\"")

generate_c_code <- function(x){
  vec <- paste0(add_doublequotes(x), sep=",\n")
  vec_tail <- str_replace(tail(vec, 1), ",\n", "\n")
  vec_head <- head(vec, length(vec) - 1)
  vec <- c(vec_head, vec_tail)

Keiku

library(dplyr)
library(purrr)
library(broom)

df <- data_frame(
  group = rep(letters[1:2], each = 50),
  cat1 = letters[round(runif(100) * 5) + 1],
  cat2 = letters[round(runif(100) * 3) + 1]
) 

Keiku

library(dplyr)
library(broom)
library(lazyeval)

df <- data_frame(
  group = rep(letters[1:2], each = 50),
  cat1 = letters[round(runif(100) * 5) + 1],
  cat2 = letters[round(runif(100) * 3) + 1]
) 

Keiku

library(gplots)
library(dplyr)
library(magrittr)

check_id_sets <- function(ids){
  ids_venn <- gplots::venn(ids, show.plot=FALSE)
  ids_list <- unlist(as.list(ids_venn))
  mat_dim <- c((length(ids_list) / (length(ids)+1)), length(ids)+1)
  id_sets <- ids_list %>%
    matrix(., mat_dim) %>%

Keiku

import time
from tqdm import tqdm

pbar = tqdm(["1", "2", "3", "4", "5"])
for char in pbar:
    pbar.set_description("Processing %s" % char)
    time.sleep(1)
#   0%|                                                     | 0/5 [00:00<?, ?it/s]
# Processing 1:  20%|██████▏                        | 1/5 [00:01<00:04,  1.00s/it]
# Processing 2:  40%|████████████▍                  | 2/5 [00:02<00:03,  1.00s/it]

Keiku

a <- c(1, 3, 5, 7, 9)
b <- c(3, 6, 8, 9, 10)
c <- c(2, 3, 4, 5, 7, 9)

intersect_all <- function(...) Reduce(intersect, list(...))
union_all <- function(...) Reduce(union, list(...))

intersect_all(a, b, c)
# [1] 3 9
union_all(a, b, c)

Keiku

library("dplyr")
library("tidyr")
library("data.table")

smp <- data_frame(
  ID = rep(1:3, 2),
  BMI = rep(c(21, 26), 3),
  sbp = rep(c(150, 120), 3),
  nendo = rep(2008:2009, 3)
)

Keiku

library(dplyr)

iris_df <- as_data_frame(iris)
iris_df %>% rename_(.dots = setNames(names(.), toupper(names(.)))) %>% head(2)
# A tibble: 2 × 5
# SEPAL.LENGTH SEPAL.WIDTH PETAL.LENGTH PETAL.WIDTH SPECIES
# <dbl>       <dbl>        <dbl>       <dbl>  <fctr>
# 1          5.1         3.5          1.4         0.2  setosa
# 2          4.9         3.0          1.4         0.2  setosa