Data Science with Python & R: Data Frames I
Motivation
These series of tutorials on Data Science will try to compare how different concepts
in the discipline can be implemented into the two dominant ecosystems nowadays: R and Python.
We will do this from a neutral point of view. Our opinion is that each environment has
good and bad things, and any data scientist should know how to use both in order to be as
prepared as possible for the job market or to start a personal project.
To get a feeling of what is going on regarding this hot topic, we refer the reader to
DataCamp's Data Science War
infographic. Their infographic explores what the strengths of R are over Python
and vice versa, and aims to provide a basic comparison between these two programming
languages from a data science and statistics perspective.
Far from being a repetition from the previous, our series of tutorials will go hands-on
into how to actually perform different data science tasks such as working with data frames,
doing aggregations, or creating different statistical models such in the areas of supervised
and unsupervised learning.
As usual, we will use real-world datasets. This will help us to quickly transfer what we
learn here to actual data analysis situations.
The first tutorial in our series will deal the an important abstraction, that of a Data Frame.
In the very next tutorial, we will introduce one of the first tasks we face when we have our data loaded, that of the Exploratory Data Analysis. This task can be performed using data frames and basic plots as we will show here for both, Python and R.
All the source code for the different parts of this series of tutorials and applications can be checked at GitHub. Feel free to get involved and share your progress with us!
What is a DataFrame?
A data frame is used for storing tabular data. It has labeled axes (rows and columns) that we can use to perform arithmetic operations at on levels.
The concept was introduced in R before it was in Python Pandas so the later repeats many of the ideas from the former. In R, a data.frame
is a list of vector variables of the same number of elements (rows) with unique row names. That is, each column is a vector with an associated name, and each row is a series of vector elements that correspond to the same position in each of the column-vectors.
In Pandas, a DataFrame
can be thought of as a dict-like container for Series
objects, where a Series
is a one-dimensional NumPy ndarray with axis labels (including time series). By default, each Series
correspond with a column in the resulting DataFrame
.
But let's see both data types in practice. First of all we will introduce a data set that will be used in order to explain the data frame creation process and what data analysis tasks can be done with a data frame. Then we will have a separate section for each platform repeating every task for you to be able to move from one to the other easily in the future.
Introducing Gapminder World Datasets
The Gapminder website presents itself as a fact-based worldview. It is a comprehensive resource for data regarding different countries and territories indicators. Its Data section contains a list of datasets that can be accessed as Google Spreadsheet pages (add &output=csv
to download as CSV). Each indicator dataset is tagged with a Data provider, a Category, and a Subcategory.
For this tutorial, we will use different datasets related to Infectious Tuberculosis:
- All TB deaths per 100K
- TB estimated prevalence (existing cases) per 100K
- TB estimated incidence (new cases) per 100K
First thing we need to do is download the files for later use within our R and Python environments. There is a description of each dataset if we click in its title in the list of datasets. When performing any data analysis task, it is essential to understand our data as much as possible, so go there and have a read. Basically, each cell in the dataset contains the data related to the number of tuberculosis cases per 100K people during the given year (column) for each country or region (row).
We will use these datasets to better understand the TB incidence in different regions in time.
Downloading Files and Reading CSV
Python
Download Google Spreadsheet data as CSV.
import urllib
tb_deaths_url_csv = 'https://docs.google.com/spreadsheets/d/12uWVH_IlmzJX_75bJ3IH5E-Gqx6-zfbDKNvZqYjUuso/pub?gid=0&output=CSV'
tb_existing_url_csv = 'https://docs.google.com/spreadsheets/d/1X5Jp7Q8pTs3KLJ5JBWKhncVACGsg5v4xu6badNs4C7I/pub?gid=0&output=csv'
tb_new_url_csv = 'https://docs.google.com/spreadsheets/d/1Pl51PcEGlO9Hp4Uh0x2_QM0xVb53p2UDBMPwcnSjFTk/pub?gid=0&output=csv'
local_tb_deaths_file = 'tb_deaths_100.csv'
local_tb_existing_file = 'tb_existing_100.csv'
local_tb_new_file = 'tb_new_100.csv'
deaths_f = urllib.urlretrieve(tb_deaths_url_csv, local_tb_deaths_file)
existing_f = urllib.urlretrieve(tb_existing_url_csv, local_tb_existing_file)
new_f = urllib.urlretrieve(tb_new_url_csv, local_tb_new_file)
Read CSV into DataFrame
by using read_csv()
.
import pandas as pd
deaths_df = pd.read_csv(local_tb_deaths_file, index_col = 0, thousands = ',').T
existing_df = pd.read_csv(local_tb_existing_file, index_col = 0, thousands = ',').T
new_df = pd.read_csv(local_tb_new_file, index_col = 0, thousands = ',').T
We have specified index_col
to be 0 since we want the country names to be the row labels. We also specified the thousands
separator to be ',' so Pandas automatically parses cells as numbers. Then, we transpose the table to make the time series for each country correspond to each column.
We will concentrate on the existing cases for a while. We can use head()
to check the first few lines.
existing_df.head()
TB prevalence, all forms (per 100 000 population per year) | Afghanistan | Albania | Algeria | American Samoa | Andorra | Angola | Anguilla | Antigua and Barbuda | Argentina | Armenia | ... | Uruguay | Uzbekistan | Vanuatu | Venezuela | Viet Nam | Wallis et Futuna | West Bank and Gaza | Yemen | Zambia | Zimbabwe |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1990 | 436 | 42 | 45 | 42 | 39 | 514 | 38 | 16 | 96 | 52 | ... | 35 | 114 | 278 | 46 | 365 | 126 | 55 | 265 | 436 | 409 |
1991 | 429 | 40 | 44 | 14 | 37 | 514 | 38 | 15 | 91 | 49 | ... | 34 | 105 | 268 | 45 | 361 | 352 | 54 | 261 | 456 | 417 |
1992 | 422 | 41 | 44 | 4 | 35 | 513 | 37 | 15 | 86 | 51 | ... | 33 | 102 | 259 | 44 | 358 | 64 | 54 | 263 | 494 | 415 |
1993 | 415 | 42 | 43 | 18 | 33 | 512 | 37 | 14 | 82 | 55 | ... | 32 | 118 | 250 | 43 | 354 | 174 | 52 | 253 | 526 | 419 |
1994 | 407 | 42 | 43 | 17 | 32 | 510 | 36 | 13 | 78 | 60 | ... | 31 | 116 | 242 | 42 | 350 | 172 | 52 | 250 | 556 | 426 |
5 rows × 207 columns
By using the attribute columns
we can read and write column names.
existing_df.columns
Index([u'Afghanistan', u'Albania', u'Algeria', u'American Samoa', u'Andorra', u'Angola', u'Anguilla', u'Antigua and Barbuda', u'Argentina', u'Armenia', u'Australia', u'Austria', u'Azerbaijan', u'Bahamas', u'Bahrain', u'Bangladesh', u'Barbados', u'Belarus', u'Belgium', u'Belize', u'Benin', u'Bermuda', u'Bhutan', u'Bolivia', u'Bosnia and Herzegovina', u'Botswana', u'Brazil', u'British Virgin Islands', u'Brunei Darussalam', u'Bulgaria', u'Burkina Faso', u'Burundi', u'Cambodia', u'Cameroon', u'Canada', u'Cape Verde', u'Cayman Islands', u'Central African Republic', u'Chad', u'Chile', u'China', u'Colombia', u'Comoros', u'Congo, Rep.', u'Cook Islands', u'Costa Rica', u'Croatia', u'Cuba', u'Cyprus', u'Czech Republic', u'Cote dIvoire', u'Korea, Dem. Rep.', u'Congo, Dem. Rep.', u'Denmark', u'Djibouti', u'Dominica', u'Dominican Republic', u'Ecuador', u'Egypt', u'El Salvador', u'Equatorial Guinea', u'Eritrea', u'Estonia', u'Ethiopia', u'Fiji', u'Finland', u'France', u'French Polynesia', u'Gabon', u'Gambia', u'Georgia', u'Germany', u'Ghana', u'Greece', u'Grenada', u'Guam', u'Guatemala', u'Guinea', u'Guinea-Bissau', u'Guyana', u'Haiti', u'Honduras', u'Hungary', u'Iceland', u'India', u'Indonesia', u'Iran', u'Iraq', u'Ireland', u'Israel', u'Italy', u'Jamaica', u'Japan', u'Jordan', u'Kazakhstan', u'Kenya', u'Kiribati', u'Kuwait', u'Kyrgyzstan', u'Laos', ...], dtype='object')
Similarly, we can access row names by using index
.
existing_df.index
Index([u'1990', u'1991', u'1992', u'1993', u'1994', u'1995', u'1996', u'1997', u'1998', u'1999', u'2000', u'2001', u'2002', u'2003', u'2004', u'2005', u'2006', u'2007'], dtype='object')
We will use them to assign proper names to our column and index names.
deaths_df.index.names = ['year']
deaths_df.columns.names = ['country']
existing_df.index.names = ['year']
existing_df.columns.names = ['country']
new_df.index.names = ['year']
new_df.columns.names = ['country']
existing_df
country | Afghanistan | Albania | Algeria | American Samoa | Andorra | Angola | Anguilla | Antigua and Barbuda | Argentina | Armenia | ... | Uruguay | Uzbekistan | Vanuatu | Venezuela | Viet Nam | Wallis et Futuna | West Bank and Gaza | Yemen | Zambia | Zimbabwe |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
year | |||||||||||||||||||||
1990 | 436 | 42 | 45 | 42 | 39 | 514 | 38 | 16 | 96 | 52 | ... | 35 | 114 | 278 | 46 | 365 | 126 | 55 | 265 | 436 | 409 |
1991 | 429 | 40 | 44 | 14 | 37 | 514 | 38 | 15 | 91 | 49 | ... | 34 | 105 | 268 | 45 | 361 | 352 | 54 | 261 | 456 | 417 |
1992 | 422 | 41 | 44 | 4 | 35 | 513 | 37 | 15 | 86 | 51 | ... | 33 | 102 | 259 | 44 | 358 | 64 | 54 | 263 | 494 | 415 |
1993 | 415 | 42 | 43 | 18 | 33 | 512 | 37 | 14 | 82 | 55 | ... | 32 | 118 | 250 | 43 | 354 | 174 | 52 | 253 | 526 | 419 |
1994 | 407 | 42 | 43 | 17 | 32 | 510 | 36 | 13 | 78 | 60 | ... | 31 | 116 | 242 | 42 | 350 | 172 | 52 | 250 | 556 | 426 |
1995 | 397 | 43 | 42 | 22 | 30 | 508 | 35 | 12 | 74 | 68 | ... | 30 | 119 | 234 | 42 | 346 | 93 | 50 | 244 | 585 | 439 |
1996 | 397 | 42 | 43 | 0 | 28 | 512 | 35 | 12 | 71 | 74 | ... | 28 | 111 | 226 | 41 | 312 | 123 | 49 | 233 | 602 | 453 |
1997 | 387 | 44 | 44 | 25 | 23 | 363 | 36 | 11 | 67 | 75 | ... | 27 | 122 | 218 | 41 | 273 | 213 | 46 | 207 | 626 | 481 |
1998 | 374 | 43 | 45 | 12 | 24 | 414 | 36 | 11 | 63 | 74 | ... | 28 | 129 | 211 | 40 | 261 | 107 | 44 | 194 | 634 | 392 |
1999 | 373 | 42 | 46 | 8 | 22 | 384 | 36 | 9 | 58 | 86 | ... | 28 | 134 | 159 | 39 | 253 | 105 | 42 | 175 | 657 | 430 |
2000 | 346 | 40 | 48 | 8 | 20 | 530 | 35 | 8 | 52 | 94 | ... | 27 | 139 | 143 | 39 | 248 | 103 | 40 | 164 | 658 | 479 |
2001 | 326 | 34 | 49 | 6 | 20 | 335 | 35 | 9 | 51 | 99 | ... | 25 | 148 | 128 | 41 | 243 | 13 | 39 | 154 | 680 | 523 |
2002 | 304 | 32 | 50 | 5 | 21 | 307 | 35 | 7 | 42 | 97 | ... | 27 | 144 | 149 | 41 | 235 | 275 | 37 | 149 | 517 | 571 |
2003 | 308 | 32 | 51 | 6 | 18 | 281 | 35 | 9 | 41 | 91 | ... | 25 | 152 | 128 | 39 | 234 | 147 | 36 | 146 | 478 | 632 |
2004 | 283 | 29 | 52 | 9 | 19 | 318 | 35 | 8 | 39 | 85 | ... | 23 | 149 | 118 | 38 | 226 | 63 | 35 | 138 | 468 | 652 |
2005 | 267 | 29 | 53 | 11 | 18 | 331 | 34 | 8 | 39 | 79 | ... | 24 | 144 | 131 | 38 | 227 | 57 | 33 | 137 | 453 | 680 |
2006 | 251 | 26 | 55 | 9 | 17 | 302 | 34 | 9 | 37 | 79 | ... | 25 | 134 | 104 | 38 | 222 | 60 | 32 | 135 | 422 | 699 |
2007 | 238 | 22 | 56 | 5 | 19 | 294 | 34 | 9 | 35 | 81 | ... | 23 | 140 | 102 | 39 | 220 | 25 | 31 | 130 | 387 | 714 |
R
In R we use read.csv
to read CSV files into data.frame
variables. Although the R function read.csv
can work with URLs, https is a problem for R in many cases, so you need to use a package like RCurl to get around it.
library(RCurl)
## Loading required package: bitops
existing_cases_file <- getURL("https://docs.google.com/spreadsheets/d/1X5Jp7Q8pTs3KLJ5JBWKhncVACGsg5v4xu6badNs4C7I/pub?gid=0&output=csv")
existing_df <- read.csv(text = existing_cases_file, row.names=1, stringsAsFactor=F)
str(existing_df)
## 'data.frame': 207 obs. of 18 variables:
## $ X1990: chr "436" "42" "45" "42" ...
## $ X1991: chr "429" "40" "44" "14" ...
## $ X1992: chr "422" "41" "44" "4" ...
## $ X1993: chr "415" "42" "43" "18" ...
## $ X1994: chr "407" "42" "43" "17" ...
## $ X1995: chr "397" "43" "42" "22" ...
## $ X1996: int 397 42 43 0 28 512 35 12 71 74 ...
## $ X1997: int 387 44 44 25 23 363 36 11 67 75 ...
## $ X1998: int 374 43 45 12 24 414 36 11 63 74 ...
## $ X1999: int 373 42 46 8 22 384 36 9 58 86 ...
## $ X2000: int 346 40 48 8 20 530 35 8 52 94 ...
## $ X2001: int 326 34 49 6 20 335 35 9 51 99 ...
## $ X2002: int 304 32 50 5 21 307 35 7 42 97 ...
## $ X2003: int 308 32 51 6 18 281 35 9 41 91 ...
## $ X2004: chr "283" "29" "52" "9" ...
## $ X2005: chr "267" "29" "53" "11" ...
## $ X2006: chr "251" "26" "55" "9" ...
## $ X2007: chr "238" "22" "56" "5" ...
The str()
function in R gives us information about a variable type. In this case
we can see that, due to the ,
thousands separator,
some of the columns hasn't been parsed as numbers but as character.
If we want to properly work with our dataset we need to convert them to numbers.
Once we know a bit more about indexing and mapping functions, I promise you will be
able to understand the following piece of code. By know let's say that we convert
a column and assign it again to its reference in the data frame.
existing_df[c(1,2,3,4,5,6,15,16,17,18)] <-
lapply( existing_df[c(1,2,3,4,5,6,15,16,17,18)],
function(x) { as.integer(gsub(',', '', x) )})
str(existing_df)
## 'data.frame': 207 obs. of 18 variables:
## $ X1990: int 436 42 45 42 39 514 38 16 96 52 ...
## $ X1991: int 429 40 44 14 37 514 38 15 91 49 ...
## $ X1992: int 422 41 44 4 35 513 37 15 86 51 ...
## $ X1993: int 415 42 43 18 33 512 37 14 82 55 ...
## $ X1994: int 407 42 43 17 32 510 36 13 78 60 ...
## $ X1995: int 397 43 42 22 30 508 35 12 74 68 ...
## $ X1996: int 397 42 43 0 28 512 35 12 71 74 ...
## $ X1997: int 387 44 44 25 23 363 36 11 67 75 ...
## $ X1998: int 374 43 45 12 24 414 36 11 63 74 ...
## $ X1999: int 373 42 46 8 22 384 36 9 58 86 ...
## $ X2000: int 346 40 48 8 20 530 35 8 52 94 ...
## $ X2001: int 326 34 49 6 20 335 35 9 51 99 ...
## $ X2002: int 304 32 50 5 21 307 35 7 42 97 ...
## $ X2003: int 308 32 51 6 18 281 35 9 41 91 ...
## $ X2004: int 283 29 52 9 19 318 35 8 39 85 ...
## $ X2005: int 267 29 53 11 18 331 34 8 39 79 ...
## $ X2006: int 251 26 55 9 17 302 34 9 37 79 ...
## $ X2007: int 238 22 56 5 19 294 34 9 35 81 ...
Everything looks fine now. But still our dataset is a bit tricky. If we have a
look at what we got into the data frame with head
head(existing_df,3)
## X1990 X1991 X1992 X1993 X1994 X1995 X1996 X1997 X1998 X1999
## Afghanistan 436 429 422 415 407 397 397 387 374 373
## Albania 42 40 41 42 42 43 42 44 43 42
## Algeria 45 44 44 43 43 42 43 44 45 46
## X2000 X2001 X2002 X2003 X2004 X2005 X2006 X2007
## Afghanistan 346 326 304 308 283 267 251 238
## Albania 40 34 32 32 29 29 26 22
## Algeria 48 49 50 51 52 53 55 56
and nrow
and ncol
nrow(existing_df)
## [1] 207
ncol(existing_df)
## [1] 18
We see that we have a data frame with 207 observations, one for each country, and 19 variables or features, one for each year. This doesn't seem the most natural shape for this dataset. It is very unlikely that we will add new countries (observations or rows in this case) to the dataset, while is quite possible to add additional years (variables or columns in this case). If we keep it like it is, we will end up with a dataset that grows in features and not in observations, and that seems counterintuitive (and unpractical depending of the analysis we will want to do).
We won't need to do this preprocessing all the time, but there we go. Thankfully, R as a function t()
similar to the method T
in Pandas, that allows us to transpose a data.frame
variable. The result is given as a matrix
, so we need to convert it to a data frame again by using as.data.frame
.
# we will save the "trasposed" original verison for later use if needed
existing_df_t <- existing_df
existing_df <- as.data.frame(t(existing_df))
head(existing_df,3)
## Afghanistan Albania Algeria American Samoa Andorra Angola Anguilla
## X1990 436 42 45 42 39 514 38
## X1991 429 40 44 14 37 514 38
## X1992 422 41 44 4 35 513 37
## Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan
## X1990 16 96 52 7 18 58
## X1991 15 91 49 7 17 55
## X1992 15 86 51 7 16 57
## Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin
## X1990 54 120 639 8 62 16 65 140
## X1991 53 113 623 8 54 15 64 138
## X1992 52 108 608 7 59 15 62 135
## Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil
## X1990 10 924 377 160 344 124
## X1991 10 862 362 156 355 119
## X1992 9 804 347 154 351 114
## British Virgin Islands Brunei Darussalam Bulgaria Burkina Faso
## X1990 32 91 43 179
## X1991 30 91 48 196
## X1992 28 91 54 208
## Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands
## X1990 288 928 188 7 449 10
## X1991 302 905 199 7 438 10
## X1992 292 881 200 7 428 9
## Central African Republic Chad Chile China Colombia Comoros
## X1990 318 251 45 327 88 188
## X1991 336 272 41 321 85 177
## X1992 342 282 38 315 82 167
## Congo, Rep. Cook Islands Costa Rica Croatia Cuba Cyprus
## X1990 209 0 30 126 32 14
## X1991 222 10 28 123 29 13
## X1992 231 57 27 121 26 13
## Czech Republic Cote d'Ivoire Korea, Dem. Rep. Congo, Dem. Rep.
## X1990 22 292 841 275
## X1991 22 304 828 306
## X1992 22 306 815 327
## Denmark Djibouti Dominica Dominican Republic Ecuador Egypt
## X1990 12 1,485 24 183 282 48
## X1991 12 1,477 24 173 271 47
## X1992 11 1,463 24 164 259 47
## El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland
## X1990 133 169 245 50 312 68 14
## X1991 126 181 245 50 337 65 12
## X1992 119 187 242 56 351 62 11
## France French Polynesia Gabon Gambia Georgia Germany Ghana Greece
## X1990 21 67 359 350 51 15 533 30
## X1991 20 55 340 350 48 15 519 29
## X1992 19 91 325 349 50 14 502 27
## Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras
## X1990 7 103 113 241 404 39 479 141
## X1991 7 101 111 248 403 43 464 133
## X1992 7 96 108 255 402 34 453 128
## Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy
## X1990 67 5 586 443 50 88 19 11 11
## X1991 68 4 577 430 51 88 18 10 10
## X1992 70 4 566 417 56 88 18 10 10
## Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan
## X1990 10 62 19 95 125 1,026 89 90
## X1991 10 60 18 87 120 1,006 84 93
## X1992 10 58 17 85 134 986 80 93
## Laos Latvia Lebanon Lesotho Liberia Libyan Arab Jamahiriya Lithuania
## X1990 428 56 64 225 476 46 64
## X1991 424 57 64 231 473 45 66
## X1992 420 59 63 229 469 45 71
## Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Mauritania
## X1990 19 367 380 159 143 640 10 585
## X1991 18 368 376 158 130 631 9 587
## X1992 17 369 365 156 118 621 9 590
## Mauritius Mexico Micronesia, Fed. Sts. Monaco Mongolia Montserrat
## X1990 53 101 263 3 477 14
## X1991 51 93 253 3 477 14
## X1992 50 86 244 3 477 14
## Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands
## X1990 134 287 411 650 170 629 11
## X1991 130 313 400 685 285 607 10
## X1992 127 328 389 687 280 585 10
## Netherlands Antilles New Caledonia New Zealand Nicaragua Niger
## X1990 28 112 10 145 317
## X1991 27 107 10 137 318
## X1992 25 104 9 129 319
## Nigeria Niue Northern Mariana Islands Norway Oman Pakistan Palau
## X1990 282 118 142 8 40 430 96
## X1991 307 115 201 8 36 428 66
## X1992 321 113 301 8 29 427 43
## Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal
## X1990 74 498 95 394 799 88 51
## X1991 73 498 93 368 783 87 49
## X1992 71 497 92 343 766 86 47
## Puerto Rico Qatar Korea, Rep. Moldova Romania Russian Federation
## X1990 17 71 223 105 118 69
## X1991 15 69 196 99 125 64
## X1992 17 69 174 103 134 70
## Rwanda Saint Kitts and Nevis Saint Lucia
## X1990 190 17 26
## X1991 211 17 26
## X1992 226 16 25
## Saint Vincent and the Grenadines Samoa San Marino
## X1990 45 36 9
## X1991 45 35 9
## X1992 44 34 8
## Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone
## X1990 346 68 380 113 465
## X1991 335 60 379 110 479
## X1992 325 59 379 106 492
## Singapore Slovakia Slovenia Solomon Islands Somalia South Africa
## X1990 52 55 66 625 597 769
## X1991 52 56 62 593 587 726
## X1992 53 59 59 563 577 676
## Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland
## X1990 44 109 409 109 629 5 14
## X1991 42 106 404 100 590 5 13
## X1992 40 104 402 79 527 6 12
## Syrian Arab Republic Tajikistan Thailand Macedonia, FYR Timor-Leste
## X1990 94 193 336 92 706
## X1991 89 162 319 90 694
## X1992 84 112 307 89 681
## Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan
## X1990 702 139 45 17 49 83 105
## X1991 687 140 44 17 46 79 99
## X1992 668 143 43 17 49 77 101
## Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates
## X1990 42 593 206 67 47
## X1991 40 573 313 64 44
## X1992 37 554 342 67 42
## United Kingdom Tanzania Virgin Islands (U.S.)
## X1990 9 215 30
## X1991 9 228 28
## X1992 10 240 27
## United States of America Uruguay Uzbekistan Vanuatu Venezuela
## X1990 7 35 114 278 46
## X1991 7 34 105 268 45
## X1992 7 33 102 259 44
## Viet Nam Wallis et Futuna West Bank and Gaza Yemen Zambia Zimbabwe
## X1990 365 126 55 265 436 409
## X1991 361 352 54 261 456 417
## X1992 358 64 54 263 494 415
Row names are sort of what in Pandas we get when we use the attribute .index
in a data frame.
rownames(existing_df)
## [1] "X1990" "X1991" "X1992" "X1993" "X1994" "X1995" "X1996" "X1997"
## [9] "X1998" "X1999" "X2000" "X2001" "X2002" "X2003" "X2004" "X2005"
## [17] "X2006" "X2007"
In our data frame we see we have weird names for them. Every year is prefixed with an X. This is so because they started as column names. From the definition of a data.frame
in R, we know that each column is a vector with a variable name. A name in R cannot start with a digit, so R automatically prefixes numbers with the letter X. Right know we will leave it like it is since it doesn't really stop us from doing our analysis.
In the case of column names, they pretty much correspond to Pandas .columns
attribute in a data frame.
colnames(existing_df)
## [1] "Afghanistan" "Albania"
## [3] "Algeria" "American Samoa"
## [5] "Andorra" "Angola"
## [7] "Anguilla" "Antigua and Barbuda"
## [9] "Argentina" "Armenia"
## [11] "Australia" "Austria"
## [13] "Azerbaijan" "Bahamas"
## [15] "Bahrain" "Bangladesh"
## [17] "Barbados" "Belarus"
## [19] "Belgium" "Belize"
## [21] "Benin" "Bermuda"
## [23] "Bhutan" "Bolivia"
## [25] "Bosnia and Herzegovina" "Botswana"
## [27] "Brazil" "British Virgin Islands"
## [29] "Brunei Darussalam" "Bulgaria"
## [31] "Burkina Faso" "Burundi"
## [33] "Cambodia" "Cameroon"
## [35] "Canada" "Cape Verde"
## [37] "Cayman Islands" "Central African Republic"
## [39] "Chad" "Chile"
## [41] "China" "Colombia"
## [43] "Comoros" "Congo, Rep."
## [45] "Cook Islands" "Costa Rica"
## [47] "Croatia" "Cuba"
## [49] "Cyprus" "Czech Republic"
## [51] "Cote d'Ivoire" "Korea, Dem. Rep."
## [53] "Congo, Dem. Rep." "Denmark"
## [55] "Djibouti" "Dominica"
## [57] "Dominican Republic" "Ecuador"
## [59] "Egypt" "El Salvador"
## [61] "Equatorial Guinea" "Eritrea"
## [63] "Estonia" "Ethiopia"
## [65] "Fiji" "Finland"
## [67] "France" "French Polynesia"
## [69] "Gabon" "Gambia"
## [71] "Georgia" "Germany"
## [73] "Ghana" "Greece"
## [75] "Grenada" "Guam"
## [77] "Guatemala" "Guinea"
## [79] "Guinea-Bissau" "Guyana"
## [81] "Haiti" "Honduras"
## [83] "Hungary" "Iceland"
## [85] "India" "Indonesia"
## [87] "Iran" "Iraq"
## [89] "Ireland" "Israel"
## [91] "Italy" "Jamaica"
## [93] "Japan" "Jordan"
## [95] "Kazakhstan" "Kenya"
## [97] "Kiribati" "Kuwait"
## [99] "Kyrgyzstan" "Laos"
## [101] "Latvia" "Lebanon"
## [103] "Lesotho" "Liberia"
## [105] "Libyan Arab Jamahiriya" "Lithuania"
## [107] "Luxembourg" "Madagascar"
## [109] "Malawi" "Malaysia"
## [111] "Maldives" "Mali"
## [113] "Malta" "Mauritania"
## [115] "Mauritius" "Mexico"
## [117] "Micronesia, Fed. Sts." "Monaco"
## [119] "Mongolia" "Montserrat"
## [121] "Morocco" "Mozambique"
## [123] "Myanmar" "Namibia"
## [125] "Nauru" "Nepal"
## [127] "Netherlands" "Netherlands Antilles"
## [129] "New Caledonia" "New Zealand"
## [131] "Nicaragua" "Niger"
## [133] "Nigeria" "Niue"
## [135] "Northern Mariana Islands" "Norway"
## [137] "Oman" "Pakistan"
## [139] "Palau" "Panama"
## [141] "Papua New Guinea" "Paraguay"
## [143] "Peru" "Philippines"
## [145] "Poland" "Portugal"
## [147] "Puerto Rico" "Qatar"
## [149] "Korea, Rep." "Moldova"
## [151] "Romania" "Russian Federation"
## [153] "Rwanda" "Saint Kitts and Nevis"
## [155] "Saint Lucia" "Saint Vincent and the Grenadines"
## [157] "Samoa" "San Marino"
## [159] "Sao Tome and Principe" "Saudi Arabia"
## [161] "Senegal" "Seychelles"
## [163] "Sierra Leone" "Singapore"
## [165] "Slovakia" "Slovenia"
## [167] "Solomon Islands" "Somalia"
## [169] "South Africa" "Spain"
## [171] "Sri Lanka" "Sudan"
## [173] "Suriname" "Swaziland"
## [175] "Sweden" "Switzerland"
## [177] "Syrian Arab Republic" "Tajikistan"
## [179] "Thailand" "Macedonia, FYR"
## [181] "Timor-Leste" "Togo"
## [183] "Tokelau" "Tonga"
## [185] "Trinidad and Tobago" "Tunisia"
## [187] "Turkey" "Turkmenistan"
## [189] "Turks and Caicos Islands" "Tuvalu"
## [191] "Uganda" "Ukraine"
## [193] "United Arab Emirates" "United Kingdom"
## [195] "Tanzania" "Virgin Islands (U.S.)"
## [197] "United States of America" "Uruguay"
## [199] "Uzbekistan" "Vanuatu"
## [201] "Venezuela" "Viet Nam"
## [203] "Wallis et Futuna" "West Bank and Gaza"
## [205] "Yemen" "Zambia"
## [207] "Zimbabwe"
These two functions show a common idiom in R, where we use the same function to get a value and to assign it. For example, if we want to change row names we will do something like:
colnames(existing_df) <- new_col_names
But as we said we will leave them as they are by now.
Data Indexing
Python
There is a whole section devoted to indexing and selecting data in DataFrames
in the official documentation. Let's apply them to our Tuberculosis cases dataframe.
We can access each data frame Series
object by using its column name, as with a Python dictionary. In our case we can access each country series by its name.
existing_df['United Kingdom']
year
1990 9
1991 9
1992 10
1993 10
1994 9
1995 9
1996 9
1997 9
1998 9
1999 9
2000 9
2001 9
2002 9
2003 10
2004 10
2005 11
2006 11
2007 12
Name: United Kingdom, dtype: int64
Or just using the key value as an attribute.
existing_df.Spain
year
1990 44
1991 42
1992 40
1993 37
1994 35
1995 34
1996 33
1997 30
1998 30
1999 28
2000 27
2001 26
2002 26
2003 25
2004 24
2005 24
2006 24
2007 23
Name: Spain, dtype: int64
Or we can access multiple series passing their column names as a Python list.
existing_df[['Spain', 'United Kingdom']]
Country | Spain | United Kingdom |
---|---|---|
year | ||
1990 | 44 | 9 |
1991 | 42 | 9 |
1992 | 40 | 10 |
1993 | 37 | 10 |
1994 | 35 | 9 |
1995 | 34 | 9 |
1996 | 33 | 9 |
1997 | 30 | 9 |
1998 | 30 | 9 |
1999 | 28 | 9 |
2000 | 27 | 9 |
2001 | 26 | 9 |
2002 | 26 | 9 |
2003 | 25 | 10 |
2004 | 24 | 10 |
2005 | 24 | 11 |
2006 | 24 | 11 |
2007 | 23 | 12 |
We can also access individual cells as follows.
existing_df.Spain['1990']
44
```
Or using any Python list indexing for slicing the series.
```python
existing_df[['Spain', 'United Kingdom']][0:5]
```
| country | Spain | United Kingdom |
|---------|-------|----------------|
| year | | |
| 1990 | 44 | 9 |
| 1991 | 42 | 9 |
| 1992 | 40 | 10 |
| 1993 | 37 | 10 |
| 1994 | 35 | 9 |
With the whole DataFrame, slicing inside of [] slices the rows. This is provided largely as a convenience since it is such a common operation.
```python
existing_df[0:5]
```
| country | Afghanistan | Albania | Algeria | American Samoa | Andorra | Angola | Anguilla | Antigua and Barbuda | Argentina | Armenia | ... | Uruguay | Uzbekistan | Vanuatu | Venezuela | Viet Nam | Wallis et Futuna | West Bank and Gaza | Yemen | Zambia | Zimbabwe |
|---------|-------------|---------|---------|----------------|---------|--------|----------|---------------------|-----------|---------|-----|---------|------------|---------|-----------|----------|------------------|--------------------|-------|--------|----------|
| year | | | | | | | | | | | | | | | | | | | | | |
| 1990 | 436 | 42 | 45 | 42 | 39 | 514 | 38 | 16 | 96 | 52 | ... | 35 | 114 | 278 | 46 | 365 | 126 | 55 | 265 | 436 | 409 |
| 1991 | 429 | 40 | 44 | 14 | 37 | 514 | 38 | 15 | 91 | 49 | ... | 34 | 105 | 268 | 45 | 361 | 352 | 54 | 261 | 456 | 417 |
| 1992 | 422 | 41 | 44 | 4 | 35 | 513 | 37 | 15 | 86 | 51 | ... | 33 | 102 | 259 | 44 | 358 | 64 | 54 | 263 | 494 | 415 |
| 1993 | 415 | 42 | 43 | 18 | 33 | 512 | 37 | 14 | 82 | 55 | ... | 32 | 118 | 250 | 43 | 354 | 174 | 52 | 253 | 526 | 419 |
| 1994 | 407 | 42 | 43 | 17 | 32 | 510 | 36 | 13 | 78 | 60 | ... | 31 | 116 | 242 | 42 | 350 | 172 | 52 | 250 | 556 | 426 |
######5 rows × 207 columns
### Indexing in production Python code
As stated in the official documentation, the Python and NumPy indexing operators [] and attribute operator . provide quick and easy access to pandas data structures across a wide range of use cases. This makes interactive work intuitive, as there’s little new to learn if you already know how to deal with Python dictionaries and NumPy arrays. However, since the type of the data to be accessed isn’t known in advance, directly using standard operators has some optimization limits. For production code, it is recommended that you take advantage of the optimized pandas data access methods exposed in this section.
For example, the `.iloc` method can be used for **positional** index access.
```python
existing_df.iloc[0:2]
```
| country | Afghanistan | Albania | Algeria | American Samoa | Andorra | Angola | Anguilla | Antigua and Barbuda | Argentina | Armenia | ... | Uruguay | Uzbekistan | Vanuatu | Venezuela | Viet Nam | Wallis et Futuna | West Bank and Gaza | Yemen | Zambia | Zimbabwe |
|---------|-------------|---------|---------|----------------|---------|--------|----------|---------------------|-----------|---------|-----|---------|------------|---------|-----------|----------|------------------|--------------------|-------|--------|----------|
| year | | | | | | | | | | | | | | | | | | | | | |
| 1990 | 436 | 42 | 45 | 42 | 39 | 514 | 38 | 16 | 96 | 52 | ... | 35 | 114 | 278 | 46 | 365 | 126 | 55 | 265 | 436 | 409 |
| 1991 | 429 | 40 | 44 | 14 | 37 | 514 | 38 | 15 | 91 | 49 | ... | 34 | 105 | 268 | 45 | 361 | 352 | 54 | 261 | 456 | 417 |
######2 rows × 207 columns
While `.loc` is used for **label** access.
```python
existing_df.loc['1992':'2005']
```
| country | Afghanistan | Albania | Algeria | American Samoa | Andorra | Angola | Anguilla | Antigua and Barbuda | Argentina | Armenia | ... | Uruguay | Uzbekistan | Vanuatu | Venezuela | Viet Nam | Wallis et Futuna | West Bank and Gaza | Yemen | Zambia | Zimbabwe |
|---------|-------------|---------|---------|----------------|---------|--------|----------|---------------------|-----------|---------|-----|---------|------------|---------|-----------|----------|------------------|--------------------|-------|--------|----------|
| year | | | | | | | | | | | | | | | | | | | | | |
| 1992 | 422 | 41 | 44 | 4 | 35 | 513 | 37 | 15 | 86 | 51 | ... | 33 | 102 | 259 | 44 | 358 | 64 | 54 | 263 | 494 | 415 |
| 1993 | 415 | 42 | 43 | 18 | 33 | 512 | 37 | 14 | 82 | 55 | ... | 32 | 118 | 250 | 43 | 354 | 174 | 52 | 253 | 526 | 419 |
| 1994 | 407 | 42 | 43 | 17 | 32 | 510 | 36 | 13 | 78 | 60 | ... | 31 | 116 | 242 | 42 | 350 | 172 | 52 | 250 | 556 | 426 |
| 1995 | 397 | 43 | 42 | 22 | 30 | 508 | 35 | 12 | 74 | 68 | ... | 30 | 119 | 234 | 42 | 346 | 93 | 50 | 244 | 585 | 439 |
| 1996 | 397 | 42 | 43 | 0 | 28 | 512 | 35 | 12 | 71 | 74 | ... | 28 | 111 | 226 | 41 | 312 | 123 | 49 | 233 | 602 | 453 |
| 1997 | 387 | 44 | 44 | 25 | 23 | 363 | 36 | 11 | 67 | 75 | ... | 27 | 122 | 218 | 41 | 273 | 213 | 46 | 207 | 626 | 481 |
| 1998 | 374 | 43 | 45 | 12 | 24 | 414 | 36 | 11 | 63 | 74 | ... | 28 | 129 | 211 | 40 | 261 | 107 | 44 | 194 | 634 | 392 |
| 1999 | 373 | 42 | 46 | 8 | 22 | 384 | 36 | 9 | 58 | 86 | ... | 28 | 134 | 159 | 39 | 253 | 105 | 42 | 175 | 657 | 430 |
| 2000 | 346 | 40 | 48 | 8 | 20 | 530 | 35 | 8 | 52 | 94 | ... | 27 | 139 | 143 | 39 | 248 | 103 | 40 | 164 | 658 | 479 |
| 2001 | 326 | 34 | 49 | 6 | 20 | 335 | 35 | 9 | 51 | 99 | ... | 25 | 148 | 128 | 41 | 243 | 13 | 39 | 154 | 680 | 523 |
| 2002 | 304 | 32 | 50 | 5 | 21 | 307 | 35 | 7 | 42 | 97 | ... | 27 | 144 | 149 | 41 | 235 | 275 | 37 | 149 | 517 | 571 |
| 2003 | 308 | 32 | 51 | 6 | 18 | 281 | 35 | 9 | 41 | 91 | ... | 25 | 152 | 128 | 39 | 234 | 147 | 36 | 146 | 478 | 632 |
| 2004 | 283 | 29 | 52 | 9 | 19 | 318 | 35 | 8 | 39 | 85 | ... | 23 | 149 | 118 | 38 | 226 | 63 | 35 | 138 | 468 | 652 |
| 2005 | 267 | 29 | 53 | 11 | 18 | 331 | 34 | 8 | 39 | 79 | ... | 24 | 144 | 131 | 38 | 227 | 57 | 33 | 137 | 453 | 680 |
######14 rows × 207 columns
And we can combine that with series indexing by column.
```python
existing_df.loc[['1992','1998','2005'],['Spain','United Kingdom']]
```
| country | Spain | United Kingdom |
|---------|-------|----------------|
| 1992 | 40 | 10 |
| 1998 | 30 | 9 |
| 2005 | 24 | 11 |
This last approach is the recommended when using Pandas data frames, specially when doing assignments (something we are not doing here). Otherwise, we might have assignment problems as described [here](http://pandas-docs.github.io/pandas-docs-travis/indexing.html#why-does-the-assignment-when-using-chained-indexing-fail).
### R
Similarly to what we do in Pandas (actually Pandas is inspired in R), we can
access a `data.frame` column by its position.
```r
existing_df[,1]
```
```r
## X1990 X1991 X1992 X1993 X1994 X1995 X1996 X1997 X1998 X1999 X2000 X2001
## 436 429 422 415 407 397 397 387 374 373 346 326
## X2002 X2003 X2004 X2005 X2006 X2007
## 304 308 283 267 251 238
## 17 Levels: 238 251 267 283 304 308 326 346 373 374 387 397 407 415 ... 436
```
The position-based indexing in `R` uses the first element for the row number and
the second one for the column one. If left blank, we are telling R to get all
the row/columns. In the previous example we retrieved all the rows for the first
column (Afghanistan) in the `data.frame`. And yes, R has a **1-based** indexing
schema.
Like in Pandas, we can use column names to access columns (series in Pandas).
However R `data.frame` variables aren't exactly object and we don't use the `.`
operator but the `$` that allows accessing labels within a list.
```r
existing_df$Afghanistan
```
```r
## X1990 X1991 X1992 X1993 X1994 X1995 X1996 X1997 X1998 X1999 X2000 X2001
## 436 429 422 415 407 397 397 387 374 373 346 326
## X2002 X2003 X2004 X2005 X2006 X2007
## 304 308 283 267 251 238
## 17 Levels: 238 251 267 283 304 308 326 346 373 374 387 397 407 415 ... 436
```
An finally, since a `data.frame` is a list of elements (its columns), we can access
columns as list elements using the list indexing operator `[[]]`.
```r
existing_df[[1]]
```
```r
## X1990 X1991 X1992 X1993 X1994 X1995 X1996 X1997 X1998 X1999 X2000 X2001
## 436 429 422 415 407 397 397 387 374 373 346 326
## X2002 X2003 X2004 X2005 X2006 X2007
## 304 308 283 267 251 238
## 17 Levels: 238 251 267 283 304 308 326 346 373 374 387 397 407 415 ... 436
```
At this point you should have realised that in R there are multiple ways of doing
the same thing, and that this seems to happen more because of the language itself
than because somebody wanted to provide different ways of doing things. This strongly
contrasts with Python's philosophy of having one clear way of doing things (the
Pythonic way).
For row indexing we have the positional approach.
```r
existing_df[1,]
```
```r
## Afghanistan Albania Algeria American Samoa Andorra Angola Anguilla
## X1990 436 42 45 42 39 514 38
## Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan
## X1990 16 96 52 7 18 58
## Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin
## X1990 54 120 639 8 62 16 65 140
## Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil
## X1990 10 924 377 160 344 124
## British Virgin Islands Brunei Darussalam Bulgaria Burkina Faso
## X1990 32 91 43 179
## Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands
## X1990 288 928 188 7 449 10
## Central African Republic Chad Chile China Colombia Comoros
## X1990 318 251 45 327 88 188
## Congo, Rep. Cook Islands Costa Rica Croatia Cuba Cyprus
## X1990 209 0 30 126 32 14
## Czech Republic Cote d'Ivoire Korea, Dem. Rep. Congo, Dem. Rep.
## X1990 22 292 841 275
## Denmark Djibouti Dominica Dominican Republic Ecuador Egypt
## X1990 12 1,485 24 183 282 48
## El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland
## X1990 133 169 245 50 312 68 14
## France French Polynesia Gabon Gambia Georgia Germany Ghana Greece
## X1990 21 67 359 350 51 15 533 30
## Grenada Guam Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras
## X1990 7 103 113 241 404 39 479 141
## Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy
## X1990 67 5 586 443 50 88 19 11 11
## Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan
## X1990 10 62 19 95 125 1,026 89 90
## Laos Latvia Lebanon Lesotho Liberia Libyan Arab Jamahiriya Lithuania
## X1990 428 56 64 225 476 46 64
## Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Mauritania
## X1990 19 367 380 159 143 640 10 585
## Mauritius Mexico Micronesia, Fed. Sts. Monaco Mongolia Montserrat
## X1990 53 101 263 3 477 14
## Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands
## X1990 134 287 411 650 170 629 11
## Netherlands Antilles New Caledonia New Zealand Nicaragua Niger
## X1990 28 112 10 145 317
## Nigeria Niue Northern Mariana Islands Norway Oman Pakistan Palau
## X1990 282 118 142 8 40 430 96
## Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal
## X1990 74 498 95 394 799 88 51
## Puerto Rico Qatar Korea, Rep. Moldova Romania Russian Federation
## X1990 17 71 223 105 118 69
## Rwanda Saint Kitts and Nevis Saint Lucia
## X1990 190 17 26
## Saint Vincent and the Grenadines Samoa San Marino
## X1990 45 36 9
## Sao Tome and Principe Saudi Arabia Senegal Seychelles Sierra Leone
## X1990 346 68 380 113 465
## Singapore Slovakia Slovenia Solomon Islands Somalia South Africa
## X1990 52 55 66 625 597 769
## Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland
## X1990 44 109 409 109 629 5 14
## Syrian Arab Republic Tajikistan Thailand Macedonia, FYR Timor-Leste
## X1990 94 193 336 92 706
## Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan
## X1990 702 139 45 17 49 83 105
## Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates
## X1990 42 593 206 67 47
## United Kingdom Tanzania Virgin Islands (U.S.)
## X1990 9 215 30
## United States of America Uruguay Uzbekistan Vanuatu Venezuela
## X1990 7 35 114 278 46
## Viet Nam Wallis et Futuna West Bank and Gaza Yemen Zambia Zimbabwe
## X1990 365 126 55 265 436 409
```
There we retrieved data for every country in 1990. We can combine this with a
column number.
```r
existing_df[1,1]
```
```r
## X1990
## 436
## 17 Levels: 238 251 267 283 304 308 326 346 373 374 387 397 407 415 ... 436
```
Or its name.
```r
existing_df$Afghanistan[1]
```
```r
## X1990
## 436
## 17 Levels: 238 251 267 283 304 308 326 346 373 374 387 397 407 415 ... 436
```
What did just do before? Basically we retrieved a column, that is a vector, and
accessed that vector first element. That way we got the value for Afghanistan for
the year 1990. We can do the same thing using the `[[]]` operator instead of the
list element label.
```r
existing_df[[1]][1]
```
```r
## X1990
## 436
## 17 Levels: 238 251 267 283 304 308 326 346 373 374 387 397 407 415 ... 436
```
We can also select multiple columns and/or rows by passing R vectors.
```r
existing_df[c(3,9,16),c(170,194)]
```
```r
## Spain United Kingdom
## X1992 40 10
## X1998 30 9
## X2005 24 11
```
Finally, using names is also possible when using positional indexing.
```r
existing_df["X1992","Spain"]
```
```r
## X1992
## 40
## Levels: 25 26 27 28 30 33 23 24 34 35 37 40 42 44
```
That we can combine with vectors.
```r
existing_df[c("X1992", "X1998", "X2005"), c("Spain", "United Kingdom")]
```
```r
## Spain United Kingdom
## X1992 40 10
## X1998 30 9
## X2005 24 11
```
##Next Steps
So enough about indexing. In the [next part of the tutorial on data frames](https://www.codementor.io/python/tutorial/python-vs-r-data-science-data-frames-ii) we will see how to perform more complex data accessing using selection. Additionally, we will explain how to apply functions to a data frame elements, and how to group them.
Remember that all the source code for the different parts of this series of tutorials and applications can be [checked at GitHub](https://github.com/jadianes/data-science-your-way). Feel free to get involved and share your progress with us!