Question

Use pd.crosstab() to count the number of regions of each cover type there are for each of the 40 soil types. Pass this function the Cover_Type column as its first argument and the Soil_Type column as the second argument. Store the results in a DataFrame named ct_by_st and then display this DataFrame.

soil = np.unique(fc['Soil_Type'])

palette = ['orchid', 'lightcoral', 'orange', 'gold', 'lightgreen', 'deepskyblue', 'cornflowerblue']

Perform the following steps in a single cell:

1. Start by converting the count information into proportions. Create a DataFrame named ct_by_st_props by dividing ct_by_st by the column sums of ct_by_st. The column sums can be calculated using np.sum() or the DataFrame sum() method.

2. We will be creating a stacked bar chart, so we need to know where the bottom of each bar should be located. This can be calculated as follow: bb = np.cumsum(ct_by_st_props) - ct_by_st_props

3. Create a Matplotlib figure, setting the figure size to [8, 4].

4. Loop over the rows of ct_by_st_props. Each time this loop executes, add a bar chart to the figure according to the following specifications.

• The height of the bars should be determined by the current row of ct_by_st_props.

• The bottom position of each bar should be determined by the current row of bb.

• Each bar should have a black border, and a fill color determined by the current value of palette.

• The label for the legend should be set to the value of Cover_Type associated with the current row.

5. Set the labels for the x and y axes to be "Soil_Type" and "Cover_Type". Set the title to be "Distribution of Cover Type by Soil Type". 6. Add a legend to the plot. Set the bbox_to_anchor parameter to place the legend to the right of the plot, near the top. 7. Display the figure using plt.show().

Elevation   Aspect   Slope   Hori Hydrology Vertical Hori Roadways   Hill_9am   Hill_Noon   Hill_3pm   Hori Points   Wilderness_Area   Soil_Type   Cover_Type
2596 51 3 258 0 510 221 232 148 6279 Rawah 29 5
2590 56 2 212 -6 390 220 235 151 6225 Rawah 29 5
2804 139 9 268 65    3180 234 238 135 6121 Rawah 12 2
2327 188 15 339 144 1256 220 250 159 1101 Cache la Poudre 6 4
2298 129 21 255 115 1326 249 222 90 999 Cache la Poudre 3 4
2289 133 21 234 106 1345 248 225 95 973 Cache la Poudre 3 4
2274 142 23 201 111 1383 246 227 96 924 Cache la Poudre 3 4

2850 359 12 30 4 1585 202 218 153 1187 Comanche Peak 31 5
2888 311 14 95 9 1774 180   229 188 1418 Comanche Peak 32 5
2903 0 5 134 19 1865 212 230 156 1463 Comanche Peak 32 5
2902 7 8 170 11 1892 211 225 151 1480 Comanche Peak 32 5

3598 20 15 342 61 1848 208 207 133 1673 Neota 40 7
3318 96 12 95 -5 1224 239 222 111 1411 Neota 38 7
3433 342 14 551 204 1044 189 217 166 1442 Neota 40 7
3218 49 18 0 0 1822 225 197 100 1673 Neota 23 2

Answer 1

ANSWER:

I have provided the properly commented  and indented code so you can easily copy the code as well as check for correct indentation.
I have provided the output image of the code so you can easily cross-check for the correct output of the code.
Have a nice and healthy day!!

CODE

# import important modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

fc = pd.read_csv("ct_by_st.csv")
# using pd.crosstab with input args as cover_type and soil type
ct_by_st = pd.crosstab(fc['Cover_Type'],fc['Soil_Type'])
# displaying dataframe
print("ct_by_st Dataframe is:")
print(ct_by_st)

palette = ['orchid', 'lightcoral', 'orange', 'gold', 'lightgreen', 'deepskyblue', 'cornflowerblue']


# 1. defining ct_by_st_props dataframe
ct_by_st_props = ct_by_st/ct_by_st.sum()

# 2. botton of each bar
bb = np.cumsum(ct_by_st_props) - ct_by_st_props

# 3. create Matplotlib figure, setting the figure size to [8, 4].
fig = plt.figure(figsize = (8,4))

# 4. loop over the rows of ct_by_st_props
for i in range(len(ct_by_st_props)):
    # fetching row with respect to index
    row_ct = ct_by_st_props.iloc[i].values
    row_bb = bb.iloc[i].values
    Cover_Type = ct_by_st_props.index[i]
    #
    plt.bar(list(range(len(row_ct))), row_ct, bottom=row_bb,label=palette[Cover_Type-1])

# 5. labeling plot
# setting xticks value
plt.xticks(list(range(len(ct_by_st_props.columns))), ct_by_st_props.columns)
# other labeling
plt.xlabel("Soil_Type")
plt.ylabel("Cover_Type")
plt.title("Distribution of Cover Type by Soil Type")

# 6. show legend
plt.legend(bbox_to_anchor=[1, 1],loc='upper right')

# 7. show plot
plt.show()

OUTPUT IMAGE