Guided Project: Customizing Data Visualizations

Posted on Wed 08 July 2015 in Projects

Introduction to the data

In [46]:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
recent_grads = pd.read_csv("recent-grads.csv")
recent_grads.head()
Out[46]:
Rank Major_code Major Total Men Women Major_category ShareWomen Sample_size Employed ... Part_time Full_time_year_round Unemployed Unemployment_rate Median P25th P75th College_jobs Non_college_jobs Low_wage_jobs
0 1 2419 PETROLEUM ENGINEERING 2339 2057 282 Engineering 0.120564 36 1976 ... 270 1207 37 0.018381 110000 95000 125000 1534 364 193
1 2 2416 MINING AND MINERAL ENGINEERING 756 679 77 Engineering 0.101852 7 640 ... 170 388 85 0.117241 75000 55000 90000 350 257 50
2 3 2415 METALLURGICAL ENGINEERING 856 725 131 Engineering 0.153037 3 648 ... 133 340 16 0.024096 73000 50000 105000 456 176 0
3 4 2417 NAVAL ARCHITECTURE AND MARINE ENGINEERING 1258 1123 135 Engineering 0.107313 16 758 ... 150 692 40 0.050125 70000 43000 80000 529 102 0
4 5 2405 CHEMICAL ENGINEERING 32260 21239 11021 Engineering 0.341631 289 25694 ... 5180 16697 1672 0.061098 65000 50000 75000 18314 4440 972

5 rows × 21 columns

In [47]:
recent_grads.tail()
Out[47]:
Rank Major_code Major Total Men Women Major_category ShareWomen Sample_size Employed ... Part_time Full_time_year_round Unemployed Unemployment_rate Median P25th P75th College_jobs Non_college_jobs Low_wage_jobs
168 169 3609 ZOOLOGY 8409 3050 5359 Biology & Life Science 0.637293 47 6259 ... 2190 3602 304 0.046320 26000 20000 39000 2771 2947 743
169 170 5201 EDUCATIONAL PSYCHOLOGY 2854 522 2332 Psychology & Social Work 0.817099 7 2125 ... 572 1211 148 0.065112 25000 24000 34000 1488 615 82
170 171 5202 CLINICAL PSYCHOLOGY 2838 568 2270 Psychology & Social Work 0.799859 13 2101 ... 648 1293 368 0.149048 25000 25000 40000 986 870 622
171 172 5203 COUNSELING PSYCHOLOGY 4626 931 3695 Psychology & Social Work 0.798746 21 3777 ... 965 2738 214 0.053621 23400 19200 26000 2403 1245 308
172 173 3501 LIBRARY SCIENCE 1098 134 964 Education 0.877960 2 742 ... 237 410 87 0.104946 22000 20000 22000 288 338 192

5 rows × 21 columns

In [48]:
recent_grads.describe()
Out[48]:
Rank Major_code Total Men Women ShareWomen Sample_size Employed Full_time Part_time Full_time_year_round Unemployed Unemployment_rate Median P25th P75th College_jobs Non_college_jobs Low_wage_jobs
count 173.000000 173.000000 172.000000 172.000000 172.000000 172.000000 173.000000 173.000000 173.000000 173.000000 173.000000 173.000000 173.000000 173.000000 173.000000 173.000000 173.000000 173.000000 173.000000
mean 87.000000 3879.815029 39370.081395 16723.406977 22646.674419 0.522223 356.080925 31192.763006 26029.306358 8832.398844 19694.427746 2416.329480 0.068191 40151.445087 29501.445087 51494.219653 12322.635838 13284.497110 3859.017341
std 50.084928 1687.753140 63483.491009 28122.433474 41057.330740 0.231205 618.361022 50675.002241 42869.655092 14648.179473 33160.941514 4112.803148 0.030331 11470.181802 9166.005235 14906.279740 21299.868863 23789.655363 6944.998579
min 1.000000 1100.000000 124.000000 119.000000 0.000000 0.000000 2.000000 0.000000 111.000000 0.000000 111.000000 0.000000 0.000000 22000.000000 18500.000000 22000.000000 0.000000 0.000000 0.000000
25% 44.000000 2403.000000 4549.750000 2177.500000 1778.250000 0.336026 39.000000 3608.000000 3154.000000 1030.000000 2453.000000 304.000000 0.050306 33000.000000 24000.000000 42000.000000 1675.000000 1591.000000 340.000000
50% 87.000000 3608.000000 15104.000000 5434.000000 8386.500000 0.534024 130.000000 11797.000000 10048.000000 3299.000000 7413.000000 893.000000 0.067961 36000.000000 27000.000000 47000.000000 4390.000000 4595.000000 1231.000000
75% 130.000000 5503.000000 38909.750000 14631.000000 22553.750000 0.703299 338.000000 31433.000000 25147.000000 9948.000000 16891.000000 2393.000000 0.087557 45000.000000 33000.000000 60000.000000 14444.000000 11783.000000 3466.000000
max 173.000000 6403.000000 393735.000000 173809.000000 307087.000000 0.968954 4212.000000 307933.000000 251540.000000 115172.000000 199897.000000 28169.000000 0.177226 110000.000000 95000.000000 125000.000000 151643.000000 148395.000000 48207.000000
In [49]:
print(recent_grads.shape[0])
print(recent_grads.dropna().shape[0])

recent_grads = recent_grads.dropna()

173
172
In [50]:
# Scatter plot that Pandas generates
from pandas.plotting import scatter_matrix
scatter_matrix(recent_grads[["ShareWomen","Unemployment_rate"]])
Out[50]:
array([[<matplotlib.axes._subplots.AxesSubplot object at 0x10a871c88>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x10abc8dd8>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x10ab8a5f8>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x10ac514e0>]], dtype=object)

Scatter matrix, step 1

In [51]:
# First, we need to create a Figure instance and create 4 axes, corresponding to each subplot.
fig_rg = plt.figure(figsize=(8,8))
ax1_rg = fig_rg.add_subplot(2,2,1)
ax2_rg = fig_rg.add_subplot(2,2,2)
ax3_rg = fig_rg.add_subplot(2,2,3)
ax4_rg = fig_rg.add_subplot(2,2,4)

# Top left plot
ax1_rg.hist(recent_grads["ShareWomen"])

# Top right plot
ax2_rg.scatter(recent_grads["Unemployment_rate"], recent_grads["ShareWomen"])

# Bottom left plot
ax3_rg.scatter(recent_grads["ShareWomen"], recent_grads["Unemployment_rate"])

# Bottom right plot
ax4_rg.hist(recent_grads["Unemployment_rate"])

plt.show()

Scatter matrix, steps 2 and 3

In [52]:
# First, we need to create a Figure instance and create 4 axes, corresponding to each subplot.
fig_rg = plt.figure(figsize=(8,6))
ax1_rg = fig_rg.add_subplot(2,2,1)
ax2_rg = fig_rg.add_subplot(2,2,2)
ax3_rg = fig_rg.add_subplot(2,2,3)
ax4_rg = fig_rg.add_subplot(2,2,4)

# Top left plot
ax1_rg.hist(recent_grads["ShareWomen"])
# Top right plot
ax2_rg.scatter(recent_grads["Unemployment_rate"], recent_grads["ShareWomen"])
# Bottom left plot
ax3_rg.scatter(recent_grads["ShareWomen"], recent_grads["Unemployment_rate"])
# Bottom right plot
ax4_rg.hist(recent_grads["Unemployment_rate"])

# Top left plot
ax1_rg.set_ylabel("ShareWomen")
ax1_rg.get_xaxis().tick_top()
ax1_rg.get_xaxis().set_visible(False)
ax1_rg.get_yaxis().tick_left()
ax1_rg.set_ylim(0,30)
ax1_rg.set_yticklabels([0, 5, 10, 15, 20, 25, 30])

# Top right plot
ax2_rg.get_yaxis().set_visible(False)
ax2_rg.get_xaxis().set_visible(False)
ax2_rg.set_xlim(0.0, 0.20)

# Bottom left plot
ax3_rg.set_xlabel("ShareWomen")
ax3_rg.set_ylabel("Unemployment_rate")
ax3_rg.get_xaxis().tick_bottom()
ax3_rg.get_yaxis().tick_left()
ax3_rg.set_ylim(0.00, 0.20)
ax3_rg.set_yticklabels([0.00, 0.05, 0.10, 0.15])
ax3_rg.set_xlim(0.0, 1.0)
ax3_rg.set_xticklabels([0.0, 0.2, 0.4, 0.6, 0.8], rotation=90)

# Bottom right plot
ax4_rg.set_xlabel("Unemployment_rate")
ax4_rg.get_yaxis().set_visible(False)
ax4_rg.get_xaxis().tick_bottom()
ax4_rg.set_xlim(0.0, 0.20)
ax4_rg.set_xticklabels([0.00, 0.05, 0.10, 0.15, 0.20], rotation=90)

# Remove space between sub plots.
plt.subplots_adjust(wspace=0, hspace=0)
plt.show()

Grouped bar plots, part 1

In [56]:
# Pandas generated grouped bar plot
recent_grads["ShareMen"] = recent_grads["Men"] / recent_grads["Total"]
arts = recent_grads[recent_grads["Major_category"] == "Arts"]
arts.set_index("Major", inplace=True)
arts[["ShareMen", "ShareWomen"]].plot(kind="bar")
Out[56]:
<matplotlib.axes._subplots.AxesSubplot at 0x10ae25358>
In [103]:
import numpy as np
recent_grads["ShareMen"] = recent_grads["Men"] / recent_grads["Total"]

# Create figure instance and subplot
fig_rg2 = plt.figure(figsize=(6,5))
ax1_rg2 = fig_rg2.add_subplot(1,1,1)

major_names = arts["Major"].tolist()
locs = np.arange(len(major_names))
width = 0.35

men_proportions = arts["ShareMen"].tolist()
bar_1 = ax1_rg2.bar(locs, men_proportions, width)
ax1_rg2.set_xticklabels(major_names, rotation=90)

plt.show()

Grouped bar plots, part 2

In [126]:
## Part 1

import numpy as np
recent_grads["ShareMen"] = recent_grads["Men"] / recent_grads["Total"]

fig_rg2 = plt.figure(figsize=(6,5))
ax1_rg2 = fig_rg2.add_subplot(1,1,1)

arts = recent_grads[recent_grads["Major_category"] == "Arts"]
major_names = arts["Major"].tolist()
locs = np.arange(len(major_names))
width = 0.35

# List representation of these columns
men_proportions = arts["ShareMen"].tolist()
women_proportions = arts["ShareWomen"].tolist()

# Generate both the bars
bar_1 = ax1_rg2.bar(locs, men_proportions, width)
ax1_rg2.set_xticklabels(major_names, rotation=90)

## Part 2

# Each value offset by `0.35`
offset_locs = locs + width

# Set of bars for `ShareWomen`
bar_2 = ax1_rg2.bar(offset_locs, women_proportions, width, color="green")

# Align x labels with bars better
ax1_rg2.set_xticks(offset_locs)

# Create a legend
plt.legend((bar_1, bar_2), ("ShareMen", "ShareWomen"), loc="upper left")

# Display the background grid
plt.grid()
plt.show()
In [ ]:

python