R/Python Data Frames for Data Science

MATH/COSC 3570 Introduction to Data Science

Dr. Cheng-Han Yu
Department of Mathematical and Statistical Sciences
Marquette University

R Tidyverse

tidyverse 📦

• The tidyverse is a 📦 for data science.
• All packages share common design philosophy, grammar, and data structures.
• The core tidyverse packages include
  • ggplot2 - data visualisation
  • dplyr - data manipulation
  • tidyr - data tidying
  • readr - data importing
  • purrr - functional programming
  • tibble - improved data frame
  • stringr - working with strings
  • forcats - working with factors
  • lubridate - working with date/times

Source: https://github.com/spcanelon/tour-of-the-tidyverse

Workflow of Data Science with R packages

Source: https://oliviergimenez.github.io/intro_tidyverse/#7

Install and Load tidyverse 📦

• tidyverse is loading all the core packages for us!

library(tidyverse)
── Attaching core tidyverse packages ──────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.3     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ──────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package to force all conflicts to become errors

• Packages in the tidyverse change frequently. To check if updates are available:

tidyverse_update()
The following packages are out of date:
● dbplyr  (2.0.0 -> 2.1.0)
● forcats (0.5.0 -> 0.5.1)
● pillar  (1.4.7 -> 1.5.0)
Start a clean R session then run:
install.packages(c("dbplyr", "forcats", "pillar"))

Tidy Data (Data Matrix)

“Happy families are all alike; every unhappy family is unhappy in its own way.” – Leo Tolstoy

• Each variable must have its own column.
• Each observation must have its own row.
• Each value must have its own cell. (match the corresponding row observation and column variable)

Tidy Data (Data Matrix)

“Tidy datasets are all alike, but every messy dataset is messy in its own way.” – Hadley Wickham

• Each variable must have its own column.
• Each observation must have its own row.
• Each value must have its own cell. (match the corresponding row observation and column variable)

• Remember in this course we are working on the so-called tidy data.
• One of the goals of tidyverse is to make our data tidy.
• So before we actually get into tidyverse, Let’s define tidy data first.
• “Tidy datasets are all alike, but every messy dataset is messy in its own way.”
• Tidy data require that
  • Each variable must have its own column. (name-value match)
  • Each observation must have its own row. The observation could be human being or any object observed in the data set. A row corresponds to an observation.
  • Each value must have its own cell.

Why Tidy Data?

1. Each variable must have its own column.
2. Each observation must have its own row.
3. Each value must have its own cell.

• Advantages of tidy data:
  • If you store all data in a tidy way, you only need to learn the tools that work with them.
  • Placing variables in columns allows R’s vectorised nature to shine. That makes transforming tidy data feel natural.

• Practical instructions:

  Put each dataset in a data frame.

  Put each variable in a column.

• These three rules are interrelated; it’s impossible to only satisfy two of the three.
• Remember, “Tidy datasets are all alike, but every messy dataset is messy in its own way.”
• Remember, R prefer vectorized programming style. If each variable corresponds to a column vector, it is easy to transform variables using vectorised functions.
• We will learn how to make our data tidy using tidyr, dplyr and other packages in Data Wrangling weeks.

Data Frames Store Tidy Data

• Collecting information about the distributions of colors and defects in a bag of M&Ms.

Non-tidy Data

• If you import data in this format into R/Python, you will be in a mess.

Tidy Data

• Each row is for one M&M. Each variable is in each column. One value is in a cell.

• Don’t code “Red” in one place and “RED” in another. Be consistent!

• You are gonna find it difficult to deal with this data when you do later visualization and data analysis.
• Don’t code “Red” with capital R in one place and “RED” with all capital letters in another. R treats those as two values.
• consistent in labeling values for categorical variables.

Modern

Data Frames

Tibbles

• Tibbles are modern version of R data frames.
• Create a new tibble using tibble().
• It is like base::data.frame(), but with a couple differences.

df <- data.frame(x = 1:5, 
                 y = letters[1:5], 
                 z = 5:1)
df

  x y z
1 1 a 5
2 2 b 4
3 3 c 3
4 4 d 2
5 5 e 1

class(df)

[1] "data.frame"

tib <- tibble(x = 1:5, 
              y = letters[1:5], 
              z = 5:1)
tib

# A tibble: 5 × 3
      x y         z
  <int> <chr> <int>
1     1 a         5
2     2 b         4
3     3 c         3
4     4 d         2
5     5 e         1

class(tib)

[1] "tbl_df"     "tbl"        "data.frame"

• Now we are going discuss the differences between tibble and data frame.

Printing of data.frame Class

How the printing method of data.frame can be improved? (Check iris in your R console)

class(iris)

[1] "data.frame"

iris

    Sepal.Length Sepal.Width Petal.Length Petal.Width    Species
1            5.1         3.5          1.4         0.2     setosa
2            4.9         3.0          1.4         0.2     setosa
3            4.7         3.2          1.3         0.2     setosa
4            4.6         3.1          1.5         0.2     setosa
5            5.0         3.6          1.4         0.2     setosa
6            5.4         3.9          1.7         0.4     setosa
7            4.6         3.4          1.4         0.3     setosa
8            5.0         3.4          1.5         0.2     setosa
9            4.4         2.9          1.4         0.2     setosa
10           4.9         3.1          1.5         0.1     setosa
11           5.4         3.7          1.5         0.2     setosa
12           4.8         3.4          1.6         0.2     setosa
13           4.8         3.0          1.4         0.1     setosa
14           4.3         3.0          1.1         0.1     setosa
15           5.8         4.0          1.2         0.2     setosa
16           5.7         4.4          1.5         0.4     setosa
17           5.4         3.9          1.3         0.4     setosa
18           5.1         3.5          1.4         0.3     setosa
19           5.7         3.8          1.7         0.3     setosa
20           5.1         3.8          1.5         0.3     setosa
21           5.4         3.4          1.7         0.2     setosa
22           5.1         3.7          1.5         0.4     setosa
23           4.6         3.6          1.0         0.2     setosa
24           5.1         3.3          1.7         0.5     setosa
25           4.8         3.4          1.9         0.2     setosa
26           5.0         3.0          1.6         0.2     setosa
27           5.0         3.4          1.6         0.4     setosa
28           5.2         3.5          1.5         0.2     setosa
29           5.2         3.4          1.4         0.2     setosa
30           4.7         3.2          1.6         0.2     setosa
31           4.8         3.1          1.6         0.2     setosa
32           5.4         3.4          1.5         0.4     setosa
33           5.2         4.1          1.5         0.1     setosa
34           5.5         4.2          1.4         0.2     setosa
35           4.9         3.1          1.5         0.2     setosa
36           5.0         3.2          1.2         0.2     setosa
37           5.5         3.5          1.3         0.2     setosa
38           4.9         3.6          1.4         0.1     setosa
39           4.4         3.0          1.3         0.2     setosa
40           5.1         3.4          1.5         0.2     setosa
41           5.0         3.5          1.3         0.3     setosa
42           4.5         2.3          1.3         0.3     setosa
43           4.4         3.2          1.3         0.2     setosa
44           5.0         3.5          1.6         0.6     setosa
45           5.1         3.8          1.9         0.4     setosa
46           4.8         3.0          1.4         0.3     setosa
47           5.1         3.8          1.6         0.2     setosa
48           4.6         3.2          1.4         0.2     setosa
49           5.3         3.7          1.5         0.2     setosa
50           5.0         3.3          1.4         0.2     setosa
51           7.0         3.2          4.7         1.4 versicolor
52           6.4         3.2          4.5         1.5 versicolor
53           6.9         3.1          4.9         1.5 versicolor
54           5.5         2.3          4.0         1.3 versicolor
55           6.5         2.8          4.6         1.5 versicolor
56           5.7         2.8          4.5         1.3 versicolor
57           6.3         3.3          4.7         1.6 versicolor
58           4.9         2.4          3.3         1.0 versicolor
59           6.6         2.9          4.6         1.3 versicolor
60           5.2         2.7          3.9         1.4 versicolor
61           5.0         2.0          3.5         1.0 versicolor
62           5.9         3.0          4.2         1.5 versicolor
63           6.0         2.2          4.0         1.0 versicolor
64           6.1         2.9          4.7         1.4 versicolor
65           5.6         2.9          3.6         1.3 versicolor
66           6.7         3.1          4.4         1.4 versicolor
67           5.6         3.0          4.5         1.5 versicolor
68           5.8         2.7          4.1         1.0 versicolor
69           6.2         2.2          4.5         1.5 versicolor
70           5.6         2.5          3.9         1.1 versicolor
71           5.9         3.2          4.8         1.8 versicolor
72           6.1         2.8          4.0         1.3 versicolor
73           6.3         2.5          4.9         1.5 versicolor
74           6.1         2.8          4.7         1.2 versicolor
75           6.4         2.9          4.3         1.3 versicolor
76           6.6         3.0          4.4         1.4 versicolor
77           6.8         2.8          4.8         1.4 versicolor
78           6.7         3.0          5.0         1.7 versicolor
79           6.0         2.9          4.5         1.5 versicolor
80           5.7         2.6          3.5         1.0 versicolor
81           5.5         2.4          3.8         1.1 versicolor
82           5.5         2.4          3.7         1.0 versicolor
83           5.8         2.7          3.9         1.2 versicolor
84           6.0         2.7          5.1         1.6 versicolor
85           5.4         3.0          4.5         1.5 versicolor
86           6.0         3.4          4.5         1.6 versicolor
87           6.7         3.1          4.7         1.5 versicolor
88           6.3         2.3          4.4         1.3 versicolor
89           5.6         3.0          4.1         1.3 versicolor
90           5.5         2.5          4.0         1.3 versicolor
91           5.5         2.6          4.4         1.2 versicolor
92           6.1         3.0          4.6         1.4 versicolor
93           5.8         2.6          4.0         1.2 versicolor
94           5.0         2.3          3.3         1.0 versicolor
95           5.6         2.7          4.2         1.3 versicolor
96           5.7         3.0          4.2         1.2 versicolor
97           5.7         2.9          4.2         1.3 versicolor
98           6.2         2.9          4.3         1.3 versicolor
99           5.1         2.5          3.0         1.1 versicolor
100          5.7         2.8          4.1         1.3 versicolor
101          6.3         3.3          6.0         2.5  virginica
102          5.8         2.7          5.1         1.9  virginica
103          7.1         3.0          5.9         2.1  virginica
104          6.3         2.9          5.6         1.8  virginica
105          6.5         3.0          5.8         2.2  virginica
106          7.6         3.0          6.6         2.1  virginica
107          4.9         2.5          4.5         1.7  virginica
108          7.3         2.9          6.3         1.8  virginica
109          6.7         2.5          5.8         1.8  virginica
110          7.2         3.6          6.1         2.5  virginica
111          6.5         3.2          5.1         2.0  virginica
112          6.4         2.7          5.3         1.9  virginica
113          6.8         3.0          5.5         2.1  virginica
114          5.7         2.5          5.0         2.0  virginica
115          5.8         2.8          5.1         2.4  virginica
116          6.4         3.2          5.3         2.3  virginica
117          6.5         3.0          5.5         1.8  virginica
118          7.7         3.8          6.7         2.2  virginica
119          7.7         2.6          6.9         2.3  virginica
120          6.0         2.2          5.0         1.5  virginica
121          6.9         3.2          5.7         2.3  virginica
122          5.6         2.8          4.9         2.0  virginica
123          7.7         2.8          6.7         2.0  virginica
124          6.3         2.7          4.9         1.8  virginica
125          6.7         3.3          5.7         2.1  virginica
126          7.2         3.2          6.0         1.8  virginica
127          6.2         2.8          4.8         1.8  virginica
128          6.1         3.0          4.9         1.8  virginica
129          6.4         2.8          5.6         2.1  virginica
130          7.2         3.0          5.8         1.6  virginica
131          7.4         2.8          6.1         1.9  virginica
132          7.9         3.8          6.4         2.0  virginica
133          6.4         2.8          5.6         2.2  virginica
134          6.3         2.8          5.1         1.5  virginica
135          6.1         2.6          5.6         1.4  virginica
136          7.7         3.0          6.1         2.3  virginica
137          6.3         3.4          5.6         2.4  virginica
138          6.4         3.1          5.5         1.8  virginica
139          6.0         3.0          4.8         1.8  virginica
140          6.9         3.1          5.4         2.1  virginica
141          6.7         3.1          5.6         2.4  virginica
142          6.9         3.1          5.1         2.3  virginica
143          5.8         2.7          5.1         1.9  virginica
144          6.8         3.2          5.9         2.3  virginica
145          6.7         3.3          5.7         2.5  virginica
146          6.7         3.0          5.2         2.3  virginica
147          6.3         2.5          5.0         1.9  virginica
148          6.5         3.0          5.2         2.0  virginica
149          6.2         3.4          5.4         2.3  virginica
150          5.9         3.0          5.1         1.8  virginica

• print entire data set out.
• does not show much information.

Tibbles Display Better

• as_tibble() turns a data frame or matrix into a tibble.

(iris_tbl <- as_tibble(iris))  ## check iris_tbl in your R console

# A tibble: 150 × 5
  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
         <dbl>       <dbl>        <dbl>       <dbl> <fct>  
1          5.1         3.5          1.4         0.2 setosa 
2          4.9         3            1.4         0.2 setosa 
3          4.7         3.2          1.3         0.2 setosa 
4          4.6         3.1          1.5         0.2 setosa 
5          5           3.6          1.4         0.2 setosa 
6          5.4         3.9          1.7         0.4 setosa 
# ℹ 144 more rows

• Only shows the first couple of rows.

• Prints data size and column type.

• Only shows the first 10 rows and all the columns that fit on one screen. This makes it easier to work with large data.
• Prints data size and column type.
• Control the default appearance, like options(tibble.print_max = n, tibble.print_min = m): if there are more than \(n\) rows, print only the first \(m\) rows.

https://stackoverflow.com/questions/36848785/how-do-i-reset-all-options-arguments-to-their-default-values

Subsets of base::data.frame May Not be Data Frames

• Sometimes [] returns a data frame and sometimes it just returns a vector.

df <- data.frame(x = 1:3, 
                 y = 3:1, 
                 z = LETTERS[1:3])
df[, 1:2]

  x y
1 1 3
2 2 2
3 3 1

class(df[, 1:2])

[1] "data.frame"

df[, 1]

[1] 1 2 3

class(df[, 1])

[1] "integer"

Treat the df as a list. How do we grab the 1st column and preserve its data frame type?

df[1]

  x
1 1
2 2
3 3

• Remember we can use [] to subset a data frame. Right?
• df[, 1, drop=FALSE]

Subsets of Tibbles Are Tibbles

• [] always returns another tibble.

df_tbl <- tibble(x = 1:2, y = 2:1, 
                 z = LETTERS[1:2])
df_tbl[, 1]

# A tibble: 2 × 1
      x
  <int>
1     1
2     2

df_tbl[1]

# A tibble: 2 × 1
      x
  <int>
1     1
2     2

class(df_tbl[, 1])

[1] "tbl_df"     "tbl"        "data.frame"

class(df_tbl[1])

[1] "tbl_df"     "tbl"        "data.frame"

• $ and [[]] return a vector.

df_tbl$x

[1] 1 2

class(df_tbl$x) 

[1] "integer"

df_tbl[[1]]

[1] 1 2

class(df_tbl[[1]])  

[1] "integer"

• Tibbles are quite strict about subsetting

Tibbles Never Do Partial Matching

• Data frames do partial matching

• Treat name “a” as “abc”!

(df <- data.frame(abc = 1))

  abc
1   1

df$a

[1] 1

• Tibbles never do partial matching

• Name “a” cannot be recognized!

(tib <- tibble(abc = 1))

# A tibble: 1 × 1
    abc
  <dbl>
1     1

tib$a

Warning: Unknown or uninitialised column: `a`.

NULL

Tibbles Can Have Complex Entries

• Data frame: Columns can’t be defined using other created variables.

data.frame(x = 1:5, 
           y = 1:5, 
           z = x + 3)
# object 'x' not found

• Tibble: Allow to refer to created variables.

tibble(x = 1:5, 
       y = 1:5, 
       z = x + 3)

# A tibble: 5 × 3
      x     y     z
  <int> <int> <dbl>
1     1     1     4
2     2     2     5
3     3     3     6
4     4     4     7
5     5     5     8

• Columns need to be atomic vectors of numbers, strings, or logical values
• Can have more complex objects, such as lists or functions.

Pipe Operator

Pipes

• The pipe %>% comes from the magrittr package of tidyverse.

• R (4.1+) has native base pipe operator |>. Tools > Global Options > Code

Note

For simple cases |> and %>% behave identically. The base pipe is recommended because we can use |> anywhere anytime in R, even we don’t use tidyverse.

https://r4ds.hadley.nz/data-transform.html#sec-the-pipe

What and How to Use Pipe

• To add the pipe, use keyboard shortcut Ctrl/Cmd + Shift + M

• The pipe sends the result of the left side of the pipe to be the first argument of the function on the right side of the pipe.

16 |> sqrt() |> log2()

[1] 2

log2(sqrt(16))

[1] 2

## We can define other arguments as if the first argument is already defined
16 |> sqrt() |> log(base = 2)

[1] 2

Why Pipe Operator?

• Nested vs. Sequential-piped
• More natural and easier-to-read structure

Source: https://www.andrewheiss.com/

• Nested vs. Sequential-piped
• Writing it out using pipes give it a more natural and easier-to-read structure.
• You can think about the following sequence of actions - wake up, get out of bed, get dressed, and leave house.
• Expressed as a set of nested functions in R would look like
• Writing it out using pipes give it a more natural and easier to read structure (if you think so)
• Like if-else statements, people don’t like too many nested layers. We can easily get lost about what is actually doing.
• Using pipe operator, we have a clear sequential order, and all the actions or functions will be shown at the beginning of each line, making the code easier to read.

08-Tibbles and Pipes

In lab.qmd ## Lab 8 section,

• Compare and contrast the following operations on a data.frame and equivalent tibble. What are the differences? Please comment.

df <- data.frame(abc = 1:2, 
                 xyz = c("a", "b"))
# list method
df$x
df[[2]]
df["xyz"]
df[c("abc", "xyz")]
# matrix method
df[, 2]
df[, "xyz"]
df[, c("abc", "xyz")]

tib <- tibble(abc = 1:2, 
              xyz = c("a", "b"))
# list method
tib$x
tib[[2]]
tib["xyz"]
tib[c("abc", "xyz")]
# matrix method
tib[, 2]
tib[, "xyz"]
tib[, c("abc", "xyz")]

• Use |> to first select last 12 rows of iris data set using tail(), then provides summary statistics on its columns using summary().

Pandas

Pandas

• Like tidyverse in R, pandas is a Python library that provides data structures, manipulation and analysis tools for data science.

import numpy as np
import pandas as pd

Pandas Data Frame

• Create a data frame from a dictionary

data = {"math": [99, 65, 87], "stats": [92, 48, 88], "cs": [50, 88, 94]}

df = pd.DataFrame(data)
print(df) 

   math  stats  cs
0    99     92  50
1    65     48  88
2    87     88  94

• Row and column names

df.index = ["s1", "s2", "s3"]
df.columns = ["Math", "Stat", "CS"]
df

    Math  Stat  CS
s1    99    92  50
s2    65    48  88
s3    87    88  94

Subsetting Columns

Warning

• In Python, [] returns Series, [[]] returns DataFrame!
• In R, [] returns tibble, [[]] returns vector!

## Series
df["Math"]

s1    99
s2    65
s3    87
Name: Math, dtype: int64

type(df["Math"])

<class 'pandas.core.series.Series'>

# ## DataFrame
df[["Math"]]

    Math
s1    99
s2    65
s3    87

type(df[["Math"]])

<class 'pandas.core.frame.DataFrame'>

df[["Math", "CS"]]

    Math  CS
s1    99  50
s2    65  88
s3    87  94

isinstance(df[[“Math”]], pd.DataFrame)

Subsetting Rows DataFrame.iloc

• integer-location based indexing for selection by position

df 

    Math  Stat  CS
s1    99    92  50
s2    65    48  88
s3    87    88  94

## first row Series
df.iloc[0] 

Math    99
Stat    92
CS      50
Name: s1, dtype: int64

## first row DataFrame
df.iloc[[0]]

    Math  Stat  CS
s1    99    92  50

## first 2 rows
df.iloc[[0, 1]]

    Math  Stat  CS
s1    99    92  50
s2    65    48  88

## 1st and 3rd row
df.iloc[[True, False, True]]

    Math  Stat  CS
s1    99    92  50
s3    87    88  94

Subsetting Rows and Columns DataFrame.iloc

df 

    Math  Stat  CS
s1    99    92  50
s2    65    48  88
s3    87    88  94

## (1, 3) row and (1, 3) col
df.iloc[[0, 2], [0, 2]]

    Math  CS
s1    99  50
s3    87  94

## all rows and 1st col
df.iloc[:, [True, False, False]]

    Math
s1    99
s2    65
s3    87

df.iloc[0:2, 1:3]

    Stat  CS
s1    92  50
s2    48  88

Subsetting Rows and Columns DataFrame.loc

Access a group of rows and columns by label(s)

df 

    Math  Stat  CS
s1    99    92  50
s2    65    48  88
s3    87    88  94

df.loc['s1', "CS"]

50

## all rows and 1st col
df.loc['s1':'s3', [True, False, False]]

    Math
s1    99
s2    65
s3    87

df.loc['s2', ['Math', 'Stat']]

Math    65
Stat    48
Name: s2, dtype: int64

Obtain a Single Cell Value DataFrame.iat/ DataFrame.at

df 

    Math  Stat  CS
s1    99    92  50
s2    65    48  88
s3    87    88  94

df.iat[1, 2]

88

df.iloc[0].iat[1]

92

df.at['s2', 'Stat']

48

df.loc['s1'].at['Stat']

92

New Columns DataFrame.insert and New Rows pd.concat

df 

    Math  Stat  CS
s1    99    92  50
s2    65    48  88
s3    87    88  94

df.insert(loc = 2, 
          column = "Chem", 
          value = [77, 89, 76])
df

    Math  Stat  Chem  CS
s1    99    92    77  50
s2    65    48    89  88
s3    87    88    76  94

df1 = pd.DataFrame({
    "Math": 88, 
    "Stat": 99, 
    "Chem": 0, 
    "CS": 100
    }, index = ['s4'])

pd.concat(objs = [df, df1])

    Math  Stat  Chem   CS
s1    99    92    77   50
s2    65    48    89   88
s3    87    88    76   94
s4    88    99     0  100

pd.concat(objs = [df, df1], 
          ignore_index = True)

NumPy

NumPy for arrays/matrices

• The array object in NumPy is called ndarray.

• Use array() to create an array.

range(0, 5, 1) # a seq of number from 0 to 4 with increment of 1

range(0, 5)

list(range(0, 5, 1))

[0, 1, 2, 3, 4]

import numpy as np
arr = np.array(range(0, 5, 1)) ## One-dim array 
arr

array([0, 1, 2, 3, 4])

type(arr)

<class 'numpy.ndarray'>

• NumPy is used to work with arrays/matrices

1D Array (Vector) and 2D Array (Matrix)

• np.arange: Efficient way to create a one-dim array of sequence of numbers

np.arange(2, 5)

array([2, 3, 4])

np.arange(6, 0, -1)

array([6, 5, 4, 3, 2, 1])

• 2D array

np.array([[1, 2, 3], [4, 5, 6]])

array([[1, 2, 3],
       [4, 5, 6]])

np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])

array([[[1, 2, 3],
        [4, 5, 6]],

       [[1, 2, 3],
        [4, 5, 6]]])

np.reshape()

arr2 = np.arange(8).reshape(2, 4)
arr2

array([[0, 1, 2, 3],
       [4, 5, 6, 7]])

arr2.shape  

(2, 4)

arr2.ndim

2

arr2.size

8

Stacking Arrays

a = np.array([1, 2, 3, 4]).reshape(2, 2)
b = np.array([5, 6, 7, 8]).reshape(2, 2)

np.vstack((a, b))

array([[1, 2],
       [3, 4],
       [5, 6],
       [7, 8]])

np.hstack((a, b))

array([[1, 2, 5, 6],
       [3, 4, 7, 8]])

09-NumPy and pandas

In lab.qmd ## Lab 9 section, create a Python pandas.DataFrame equivalent to the R tibble

tibble(x = 1:5, y = 5:1, z = LETTERS[1:5])

# A tibble: 5 × 3
      x     y z    
  <int> <int> <chr>
1     1     5 A    
2     2     4 B    
3     3     3 C    
4     4     2 D    
5     5     1 E    

import numpy as np
import pandas as pd
import string as st
list(st.ascii_uppercase)
dic = {'__': np.arange(__, __), 
       '__': np.arange(__, __, __),
       '__': list(__.ascii_uppercase)[___]}
pd._____________(dic)

Lab Bonus!

• Happy Ralentine’s Day! ❤️

x <- seq(0, 2*pi, by = 0.01)
xhrt <- 16 * sin(x) ^ 3
yhrt <- 13 * cos(x) - 5 * cos(2*x) - 2 * cos(3*x) - cos(4*x)
par(mar = c(0, 0, 0, 0))
plot(xhrt, yhrt, type = "l", axes = FALSE, xlab = "", ylab = "")
polygon(xhrt, yhrt, col = "red", border = NA)
points(c(10,-10, -15, 15), c(-10, -10, 10, 10), pch = 169, font = 5)
text(0, 0, "Happy Valentine's Day!", font = 2, cex = 2, col = "pink")

Lab Bonus!

• Happy Pylentine’s Day! ❤️

lines = []
msg = "~Happy Valentine's Day!~"
for y in range(15, -15, -1):
    line = ""
    for x in range(-30, 30):
        f = ((x * 0.05) ** 2 + (y * 0.1) ** 2 - 1) ** 3 - (x * 0.05) ** 2 * (y * 0.1) ** 3
        line += msg[(x - y) % len(msg)] if f <= 0 else " "
    lines.append(line)

print("\n".join(lines))

Resources

• tibble
• pipes
• NumPy
• pandas