How to Create a Matrix from a Data Set in Seaborn with Python

In this article, we show how to create a matrix from a data set in seaborn with Python.

So many times, as convention, you load a data set in seaborn using the load_dataset() function.

Many times, these data sets have integer indexes.

However, when creating a plot such as a matrix plot, the data needs to be converted into a matrix before we can plot it.

Therefore, we need to do a matrix conversion.

We can do a matrix conversion using the pivot_table() function. With this function, we can specify which column we want to be the rows, which column we want to be the columns, and the the values of these data points.

In the following code below, we show load in the data set, flights, from seaborn. This data set is then converted into a matrix, in which the month column becomes the rows, the year column is the columns, and the passengers column are the values.

import seaborn as sns %matplotlib inline flights= sns.load_dataset('flights') flights.head() Out[12]: year month passengers 0 1949 January 112 1 1949 February 118 2 1949 March 132 3 1949 April 129 4 1949 May 121 flights.pivot_table(index='month', columns='year',values='passengers') Out[11]: year 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 \ month January 112 115 145 171 196 204 242 284 315 340 360 February 118 126 150 180 196 188 233 277 301 318 342 March 132 141 178 193 236 235 267 317 356 362 406 April 129 135 163 181 235 227 269 313 348 348 396 May 121 125 172 183 229 234 270 318 355 363 420 June 135 149 178 218 243 264 315 374 422 435 472 July 148 170 199 230 264 302 364 413 465 491 548 August 148 170 199 242 272 293 347 405 467 505 559 September 136 158 184 209 237 259 312 355 404 404 463 October 119 133 162 191 211 229 274 306 347 359 407 November 104 114 146 172 180 203 237 271 305 310 362 December 118 140 166 194 201 229 278 306 336 337 405 year 1960 month January 417 February 391 March 419 April 461 May 472 June 535 July 622 August 606 September 508 October 461 November 390 December 432

By convention, we import seaborn as sns.

In order to see the graph within the editor, we put in the statement, %matplotlib inline

You put this statement in if you are using an editor such as jupyter notebooks so that you can see the graph output in the editor.

Seaborn already has built-in data sets.

One data set that can be used is tips.

We import this dataset with the line, flights=sns.load_dataset('flights')

We then output the contents of tips using flights.head() You can see that the columns are year, month, and passenger.

We then convert this data set into a matrix using the line, flights.pivot_table(index='month', columns='year',values='passengers')

The month column becomes the rows. The year column becomes the columns. And the passengers column becomes the values.

After this function, you can now see this arrangement.

Again, this is an import conversion, because in order to plot matrix plots, the data needs to be in matrix format first.

And this is how to create a matrix from a data set in seaborn with Python.

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