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37 | 37 | " - Line type,\n", |
38 | 38 | " - Marker shape.\n", |
39 | 39 | " \n", |
40 | | - "3. **Plotting for multiple views:** Understanding complex datasets can require multiple views, sometimes even for the same relationship. These views can be as simple as a comparison plots or as plots which slice complex datasets into understandable projections. \n", |
| 40 | + "3. **Plotting with multiple views:** Understanding complex datasets can require multiple views, sometimes even for the same relationship. These views can be as simple as a comparison plots or as plots which slice complex datasets into understandable projections. \n", |
41 | 41 | " - Comparing side by side plots of subsets of a complex data,\n", |
42 | 42 | " - Pairwise scatter plots for visualization of pairwise combinations of the variables, \n", |
43 | 43 | " - Faceted, conditioned or small multiple plots are all names for a method which produces visual slices through complex data. \n" |
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205 | 205 | " ## Convert some columns to numeric values\n", |
206 | 206 | " for column in cols:\n", |
207 | 207 | " auto_prices[column] = pd.to_numeric(auto_prices[column])\n", |
208 | | - "# auto_prices[cols] = auto_prices[cols].as_type(int64)\n", |
209 | 208 | " \n", |
210 | 209 | " return auto_prices\n", |
211 | 210 | "auto_prices = read_auto_data()" |
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382 | 381 | "- A **qualitative palette** is a palette of individual colors is used to display categorical values. We will address this case latter. \n", |
383 | 382 | "- A **sequential and divergent palettes** is a sequence of colors used to display a quantitative variable. A sequential palette contains a color sequence of changing hue, which corresponds to values of a quantitative variable. A divergent palette has two sequences of hues with a gap in the center. Divergent palettes are useful for quantitative variables with positive and negative values. \n", |
384 | 383 | "\n", |
385 | | - "When doing data visualization in Python you have the choice of several sets of high quality sequential and divergent color palettes. [There is an extensive set of palette choices with Seaborn](https://seaborn.pydata.org/tutorial/color_palettes.html). Matplotlib contains a [another set of high quality palettes](https://matplotlib.org/3.1.1/tutorials/colors/colormaps.html). These Matplotlib options including **perceptually uniform palettes**, are expertly designed to optimize human perception. \n", |
| 384 | + "When doing data visualization in Python you have the choice of several sets of high quality sequential and divergent color palettes. [There is an extensive set of palette choices with Seaborn](https://seaborn.pydata.org/tutorial/color_palettes.html). Matplotlib contains a [another set of high quality palettes](https://matplotlib.org/3.1.1/tutorials/colors/colormaps.html). These Matplotlib options, including **perceptually uniform palettes**, are expertly designed to optimize human perception. \n", |
386 | 385 | "\n", |
387 | 386 | "The code in the cell below creates a scatter plot with vehicle price shown using the magma palette from Matplotlib. Magma uses lighter or 'hotter' colors to show larger values of a quantitative variable. Execute this code and examine the results. " |
388 | 387 | ] |
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