![]() ![]() And for the third row, second column, we would use. (Gotta love python for using 0 as the start of all numeric sequences instead of 1.) If we wanted to call the axis in the second row, first column, we would use. For example, indicates the axis is in the first row and first column, and indicates the axis is in the first row and second column. let's automate themīecause this figure has more than one row of subplots, the axes must be identified by instead of just. If we were going to write out code for the six plots the long way, we would start like this: fig, ax = plt.subplots(figsize=(15,10), nrows=2, ncols=3, sharey=True) fig.suptitle('Histograms', fontsize=15) ax.hist(df) ax.set_title('Bedrooms Distribution') ax.set_xlabel('number of bedrooms') ax.set_ylabel('number of homes') ax.hist(df) ax.set_title('Bathrooms Distribution') ax.set_xlabel('number of bathrooms') ax.set_ylabel('number of homes') # I don't want to write these out anymore. First, note that for figures containing multiple rows of subplots, axes must be identified by their row and column positions. It would be time-consuming to assign a plot, title, and labels for six (or more) axes, so we can use a for loop. Now for the fun part! What if you have many subplots to include? For example, let’s pick 6 columns from the King County home sales data and do a histogram for each, as we saw at the beginning of this story. This sets the y-axis of all three graphs to be the same, for ease of comparison between them. Note that the plt.subplots() function here has one additional parameter specified: sharey=True Putting it all together: fig, ax = plt.subplots(figsize=(18,6), nrows=1, ncols=3, sharey=True) fig.suptitle('Features of King County Homes Sold in 20', fontsize=21) ax.hist(df) ax.set_title('Bedrooms Distribution', fontsize=18) ax.set_xlabel('number of bedrooms', fontsize=15) ax.set_ylabel('number of homes', fontsize=15) ax.hist(df) ax.set_title('Bathrooms Distribution', fontsize=18) ax.set_xlabel('number of bathrooms', fontsize=15) ax.set_ylabel('number of homes', fontsize=15) ax.hist(df) ax.set_title('Square Feet Distribution', fontsize=18) ax.set_xlabel('living space square footage', fontsize=15) ax.set_ylabel('number of homes', fontsize=15) plt.subplots_adjust(top=0.85, wspace=0.15) The plt.subplots_adjust() function is useful for adjusting the white space between plots, and between the figure title and plots (use parameter “top” for this). ax.set_xlabel(‘number of bedrooms’) ax.set_ylabel(‘number of homes’) fig.suptitle(‘Histograms’, fontsize=15)Īdditionally, x and y axis labels can be added to each subplot, as shown below. Above, we added titles to each subplot, but what if we want a title for the whole figure? To do this, we can call the suptitle() method on the figure. ![]() For example: ax1 = plt.subplot(131) Making Subplots Readable - Labels and Paddingīefore we move on to automating subplot creation, let’s add labels to our subplots and adjust the white space between them. The commas can also be omitted, as long as the numbers are in the right order: total number of rows, total number of columns, and index of the plot. Means we are adding this plot to a figure with 1 row and 3 columns, and that this plot should be the first axis in the figure. ![]() The numbers in the subplot function refer to the parameters nrows, ncols, and index. You may see subplots defined individually using plt.subplot(): # create axes individually using plt.subplot() plt.figure(figsize = (18,6)) # need this step to set figure size ax1 = plt.subplot(1, 3, 1) df.hist() plt.title('bedrooms') ax2 = plt.subplot(1, 3, 2) df.hist() plt.title('bathrooms') ax3 = plt.subplot(1, 3, 3) df.hist() plt.title('sqft_living') plt.savefig('images/hist_no_figure') Now guess what - there is yet another way to create the exact same subplots. Defining eachĪxis in the array separately would yield the same result: # add plots to each axis - alternative code fig, = plt.subplots(figsize=(15,4), nrows=1, ncols=3) ax1.hist(df) ax1.set_title(‘bedrooms’) ax2.hist(df) ax2.set_title(‘bathrooms’) ax3.hist(df) ax3.set_title(‘sqft_living’) We called the hist and set_title methods on each item in the array ‘ax’. Here, we used the first, second, and third axes to plot histograms. ![]()
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