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MATPLOTLIB, choose a visulization - Coggle Diagram

- - - - Example:
        days_in_year = [88, 225, 365, 687, 4333, 10756, 30687, 60190, 90553]
        plt.bar(range(len(days_in_year)), days_in_year)
        plt.show()
    - - use .barplot() or .countplot()
        
        ordering
        
        sns.countplot(df["victory_status"], order=df["victory_status"].value_counts(ascending=True).index)
  - - - //#Paste the x_values code here
        n = 1 # This is our first dataset (out of 2)
        t = 2 # Number of datasets
        d = 6 # Number of sets of bars
        w = 0.8 # Width of each bar/
        store1_x = [t*element + w n for element in range(*d)]
        plt.bar(store1_x, sales1)
  - - - We do this by using the keyword bottom. The top set of bars will have bottom set to the heights of the other set of bars. So the first set of bars is plotted normally and the second set of bars has bottom specified:
        sales1 = [91, 76, 56, 66, 52, 27]
        sales2 = [65, 82, 36, 68, 38, 40]
        plt.bar(range(len(sales1)), sales1)
        plt.bar(range(len(sales2)), sales2, bottom=sales1)
  - - - If we want a different amount of error for each bar, we can make yerr equal to a list rather than a single number:
        yerr = [1, 3, 0.5, 2, 4]
  - - - То calculate upper and lowwer values, use list comprehension.
        If we have error of 2:
        y_lower = [i - 2 for i in y_values]
        y_upper = [i + 2 for i in y_values]
        
        x_values = range(10)
        y_values = [10, 12, 13, 13, 15, 19, 20, 22, 23, 29]
        y_lower = [8, 10, 11, 11, 13, 17, 18, 20, 21, 27]
        y_upper = [12, 14, 15, 15, 17, 21, 22, 24, 25, 31]
        plt.fill_between(x_values, y_lower, y_upper, alpha=0.2) #this is the shaded error
        plt.plot(x_values, y_values) #this is the line itself
        plt.show()
  - - - payment_method_freqs = [270, 77, 32, 11]
        plt.pie(payment_method_freqs)
        plt.axis('equal')
  - - - Method 1
        use a legend to label each color:
        budget_data = [500, 1000, 750, 300, 100]
        budget_categories = ['marketing', 'payroll', 'engineering', 'design', 'misc']
        plt.pie(budget_data)
        plt.legend(budget_categories)
      - Method 2
        put labels on the chart itself:
        plt.pie(budget_data, labels=budget_categories)
        
        keyword autopct:
        '%0.2f' — 2 decimal places, like 4.08.
        '%0.2f%%' — 2 decimal places, but with a percent sign at the end, like 4.08%.
        '%d%%' — rounded to the nearest int and with a percent sign at the end, like 4%.
        
        plt.pie(budget_data,
        labels=budget_categories,
        autopct='%0.1f%%')
  - - - If you want to create more spicified Histogram you can use this code:
        plt.hist(dataset, range=(66,69), bins=40)
        key word range, is displays minimum and maximum vlues from on X-axis, in our example it values from 66 to 69.
        key word bins is displays amount of equal divisions(bins) on our X-axis.
  - - - Use keyword alpha(transparency) A value of 0 would make the bars entirely transparent. A value of 1 would make the bars completely opaque.
        plt.hist(a, range=(55, 75), bins=20, alpha=0.5)
        plt.hist(b, range=(55, 75), bins=20, alpha=0.5)
        
        use the keyword histtype with the argument 'step' to draw just the outline of a histogram:
        
        Use keyword linewidth=2 to make line thicker
      - Another problem we face is that our histograms might have different numbers of samples, making one much bigger than the other. And make our histagram unreadable.
        
        To solve this, we can normalize our histograms using density=True. This command divides the height of each column by a constant such that the total shaded area of the histogram sums to 1.
        
        Sample:
        a = normal(loc=64, scale=2, size=10000)
        b = normal(loc=70, scale=2, size=100000)
        plt.hist(a, range=(55, 75), bins=20, alpha=0.5, density=True)
        plt.hist(b, range=(55, 75), bins=20, alpha=0.5, density=True)
        plt.show()
- - - - But, if we want special labels (such as strings), we can use the command ** or ax.set_yticklabels()**.
        For example, we might want to have a y-axis with ticks at 0.1, 0.6, and 0.8, but label them 10%, 60%, and 80%, respectively. To do this, we use the following commands:
        ax = plt.subplot()
        plt.plot([1, 3, 3.5], [0.1, 0.6, 0.8], 'o')
        ax.set_yticks([0.1, 0.6, 0.8])
        ax.set_yticklabels(['10%', '60%', '80%'])
- - - - x_values = [0, 1, 2, 3, 4] y_values = [0, 1, 4, 9, 16] plt.plot(x_values, y_values) plt.show()
- - - - We can also add a marker using the keyword marker:
        .# A circle:
        plt.plot(x_values, y_values, marker='o')
        .# A square:
        plt.plot(x_values, y_values, marker='s')
        .# A star:
        plt.plot(x_values, y_values, marker='*')
- - - - For example, if we want to display a plot from x=0 to x=3 and from y=2 to y=5, we would call plt.axis([0, 3, 2, 5]).
- - - - Any plt.plot() that comes after plt.subplot() will create a line plot in the specified subplot. For instance:
        Data sets
        x = [1, 2, 3, 4]
        y = [1, 2, 3, 4]
        First Subplot
        plt.subplot(1, 2, 1)
        plt.plot(x, y, color='green')
        plt.title('First Subplot')
        Second Subplot
        plt.subplot(1, 2, 2)
        plt.plot(x, y, color='steelblue')
        plt.title('Second Subplot')
        Display both subplots
        plt.show()
- - - - We can also use multiple keyword arguments, if we need to adjust multiple margins. For instance, we could adjust both the top and the hspace:
        plt.subplots_adjust(top=0.95, hspace=0.25)
- - - - Second one:
        plt.plot([0, 1, 2, 3, 4], [0, 1, 4, 9, 16], label="parabola")
        plt.plot([0, 1, 2, 3, 4], [0, 1, 8, 27, 64], label="cubic")
        plt.legend() # Still need this command!
        plt.show()
        
        plt.legend() can also take a keyword argument loc, which will position the legend on the figure.
        Example:
        plt.legend(['parabola', 'cubic'], loc=6)
        plt.show()
        
        Number Code |String
        0 best
        1 upper right
        2 upper left
        3 lower left
        4 lower right
        5 right
        6 center left
        7 center right
        8 lower center
        9 upper center
        10 center
- - - - Once we’ve created a figure, we might want to save it so that we can use it in a presentation or a website. We can use the command plt.savefig() to save out to many different file formats, such as png, svg, or pdf.
        
        After plotting, we can call plt.savefig('name_of_graph.png'):
        .# Figure 2
        plt.figure(figsize=(4, 10))
        plt.plot(x, parabola)
        plt.savefig('tall_and_narrow.png')
- - - - sns.countplot(df["victory_status"]
      - Bar Chart Ordering
        
        Nominal Data
        
        ascending order
        sns.countplot(df["victory_status"], order=df["victory_status"].value_counts(ascending=True).index)
        
        Ordinal Data
        
        sns.countplot(df["Grade Level"], order=["First Year", "Second Year", "Third Year", "Fourth Year"])