Scatter plots, bar plot and grouped bar plots
Introduction to the data
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
recent_grads = pd.read_csv("recent-grads.csv")
recent_grads.head()
| 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
recent_grads.tail()
| 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
recent_grads.describe()
| 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 |
print(recent_grads.shape[0])
print(recent_grads.dropna().shape[0])
recent_grads = recent_grads.dropna()
173
172
# Scatter plot that Pandas generates
from pandas.tools.plotting import scatter_matrix
scatter_matrix(recent_grads[["ShareWomen","Unemployment_rate"]])
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
# 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
# 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
# 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")
<matplotlib.axes._subplots.AxesSubplot at 0x10ae25358>
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
## 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()
