Data Prep and Visualization Example using matplotlib in Python

matplotlib tips
data science
Publish Date:   2021-08-25

Some simple ways to make histogram or line plots using matplotlib.

# Install the pydataset package. This package gives us data sets to work with very easily
! pip install pydataset
# The convention for importing matplotlib with an alias is "plt". We'll also need pandas and numpy

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

The Air Passengers Dataset

This dataset shows the number of passengers flying United States airlines by month from 1949-1960. 

from pydataset import data

passengers = data('AirPassengers')
passengers.head(12)

time AirPassengers
1 1949.000000 112
2 1949.083333 118
3 1949.166667 132
4 1949.250000 129
5 1949.333333 121
6 1949.416667 135
7 1949.500000 148
8 1949.583333 148
9 1949.666667 136
10 1949.750000 119
11 1949.833333 104
12 1949.916667 118

#1 Add a ‘year’ column to passengers that reflects the current year

import numpy as np

passengers['Year'] = passengers['time'].apply(lambda x: int(x))
passengers

time AirPassengers year month Year
1 1949.000000 112 1949 1.0 1949
2 1949.083333 118 1949 2.0 1949
3 1949.166667 132 1949 3.0 1949
4 1949.250000 129 1949 4.0 1949
5 1949.333333 121 1949 5.0 1949
6 1949.416667 135 1949 6.0 1949
7 1949.500000 148 1949 7.0 1949
8 1949.583333 148 1949 8.0 1949
9 1949.666667 136 1949 9.0 1949
10 1949.750000 119 1949 10.0 1949
11 1949.833333 104 1949 11.0 1949
12 1949.916667 118 1949 12.0 1949
13 1950.000000 115 1950 1.0 1950
14 1950.083333 126 1950 2.0 1950
15 1950.166667 141 1950 3.0 1950
16 1950.250000 135 1950 4.0 1950
17 1950.333333 125 1950 5.0 1950
18 1950.416667 149 1950 6.0 1950
19 1950.500000 170 1950 7.0 1950
20 1950.583333 170 1950 8.0 1950
21 1950.666667 158 1950 9.0 1950
22 1950.750000 133 1950 10.0 1950
23 1950.833333 114 1950 11.0 1950
24 1950.916667 140 1950 12.0 1950
25 1951.000000 145 1951 1.0 1951
26 1951.083333 150 1951 2.0 1951
27 1951.166667 178 1951 3.0 1951
28 1951.250000 163 1951 4.0 1951
29 1951.333333 172 1951 5.0 1951
30 1951.416667 178 1951 6.0 1951
31 1951.500000 199 1951 7.0 1951
32 1951.583333 199 1951 8.0 1951
33 1951.666667 184 1951 9.0 1951
34 1951.750000 162 1951 10.0 1951
35 1951.833333 146 1951 11.0 1951
36 1951.916667 166 1951 12.0 1951
37 1952.000000 171 1952 1.0 1952
38 1952.083333 180 1952 2.0 1952
39 1952.166667 193 1952 3.0 1952
40 1952.250000 181 1952 4.0 1952
41 1952.333333 183 1952 5.0 1952
42 1952.416667 218 1952 6.0 1952
43 1952.500000 230 1952 7.0 1952
44 1952.583333 242 1952 8.0 1952
45 1952.666667 209 1952 9.0 1952
46 1952.750000 191 1952 10.0 1952
47 1952.833333 172 1952 11.0 1952
48 1952.916667 194 1952 12.0 1952
49 1953.000000 196 1953 1.0 1953
50 1953.083333 196 1953 2.0 1953
51 1953.166667 236 1953 3.0 1953
52 1953.250000 235 1953 4.0 1953
53 1953.333333 229 1953 5.0 1953
54 1953.416667 243 1953 6.0 1953
55 1953.500000 264 1953 7.0 1953
56 1953.583333 272 1953 8.0 1953
57 1953.666667 237 1953 9.0 1953
58 1953.750000 211 1953 10.0 1953
59 1953.833333 180 1953 11.0 1953
60 1953.916667 201 1953 12.0 1953
61 1954.000000 204 1954 1.0 1954
62 1954.083333 188 1954 2.0 1954
63 1954.166667 235 1954 3.0 1954
64 1954.250000 227 1954 4.0 1954
65 1954.333333 234 1954 5.0 1954
66 1954.416667 264 1954 6.0 1954
67 1954.500000 302 1954 7.0 1954
68 1954.583333 293 1954 8.0 1954
69 1954.666667 259 1954 9.0 1954
70 1954.750000 229 1954 10.0 1954
71 1954.833333 203 1954 11.0 1954
72 1954.916667 229 1954 12.0 1954
73 1955.000000 242 1955 1.0 1955
74 1955.083333 233 1955 2.0 1955
75 1955.166667 267 1955 3.0 1955
76 1955.250000 269 1955 4.0 1955
77 1955.333333 270 1955 5.0 1955
78 1955.416667 315 1955 6.0 1955
79 1955.500000 364 1955 7.0 1955
80 1955.583333 347 1955 8.0 1955
81 1955.666667 312 1955 9.0 1955
82 1955.750000 274 1955 10.0 1955
83 1955.833333 237 1955 11.0 1955
84 1955.916667 278 1955 12.0 1955
85 1956.000000 284 1956 1.0 1956
86 1956.083333 277 1956 2.0 1956
87 1956.166667 317 1956 3.0 1956
88 1956.250000 313 1956 4.0 1956
89 1956.333333 318 1956 5.0 1956
90 1956.416667 374 1956 6.0 1956
91 1956.500000 413 1956 7.0 1956
92 1956.583333 405 1956 8.0 1956
93 1956.666667 355 1956 9.0 1956
94 1956.750000 306 1956 10.0 1956
95 1956.833333 271 1956 11.0 1956
96 1956.916667 306 1956 12.0 1956
97 1957.000000 315 1957 1.0 1957
98 1957.083333 301 1957 2.0 1957
99 1957.166667 356 1957 3.0 1957
100 1957.250000 348 1957 4.0 1957
101 1957.333333 355 1957 5.0 1957
102 1957.416667 422 1957 6.0 1957
103 1957.500000 465 1957 7.0 1957
104 1957.583333 467 1957 8.0 1957
105 1957.666667 404 1957 9.0 1957
106 1957.750000 347 1957 10.0 1957
107 1957.833333 305 1957 11.0 1957
108 1957.916667 336 1957 12.0 1957
109 1958.000000 340 1958 1.0 1958
110 1958.083333 318 1958 2.0 1958
111 1958.166667 362 1958 3.0 1958
112 1958.250000 348 1958 4.0 1958
113 1958.333333 363 1958 5.0 1958
114 1958.416667 435 1958 6.0 1958
115 1958.500000 491 1958 7.0 1958
116 1958.583333 505 1958 8.0 1958
117 1958.666667 404 1958 9.0 1958
118 1958.750000 359 1958 10.0 1958
119 1958.833333 310 1958 11.0 1958
120 1958.916667 337 1958 12.0 1958
121 1959.000000 360 1959 1.0 1959
122 1959.083333 342 1959 2.0 1959
123 1959.166667 406 1959 3.0 1959
124 1959.250000 396 1959 4.0 1959
125 1959.333333 420 1959 5.0 1959
126 1959.416667 472 1959 6.0 1959
127 1959.500000 548 1959 7.0 1959
128 1959.583333 559 1959 8.0 1959
129 1959.666667 463 1959 9.0 1959
130 1959.750000 407 1959 10.0 1959
131 1959.833333 362 1959 11.0 1959
132 1959.916667 405 1959 12.0 1959
133 1960.000000 417 1960 1.0 1960
134 1960.083333 391 1960 2.0 1960
135 1960.166667 419 1960 3.0 1960
136 1960.250000 461 1960 4.0 1960
137 1960.333333 472 1960 5.0 1960
138 1960.416667 535 1960 6.0 1960
139 1960.500000 622 1960 7.0 1960
140 1960.583333 606 1960 8.0 1960
141 1960.666667 508 1960 9.0 1960
142 1960.750000 461 1960 10.0 1960
143 1960.833333 390 1960 11.0 1960
144 1960.916667 432 1960 12.0 1960

#2 Add a “month” column


passengers['month'] = (passengers['time'] -passengers['year'])*12+1
passengers['Month']= (passengers['Year']-min(passengers['Year']))*12+passengers['month']

#3 Generate the plot below of passengers vs. time using each monthly count

temp=passengers.groupby(['Month'])['AirPassengers'].sum().reset_index()


plt.figure(figsize=(8,6))
plt.plot(temp['Month'],temp['AirPassengers'])
plt.xlabel("Month", size=14)
plt.ylabel("Hundreds of thousands", size=14)

plt.title("plot with Matplotlib")
Text(0.5, 1.0, 'plot with Matplotlib')

png

#4 Generate the plot below of passengers vs. time using an annual count

temp=passengers.groupby(['Year'])['AirPassengers'].sum().reset_index()

plt.figure(figsize=(8,6))
plt.plot(temp['Year'],temp['AirPassengers'])
plt.xlabel("Year", size=14)
plt.ylabel("Hundreds of thousands", size=14)

plt.title("plot with Matplotlib")
Text(0.5, 1.0, 'plot with Matplotlib')

png

#5 Generate the barplot below of passengers by year

temp=passengers.groupby(['Year'])['AirPassengers'].sum().reset_index()

plt.figure(figsize=(8,6))
plt.bar(temp['Year'],temp['AirPassengers'])
plt.xlabel("Year", size=14)
plt.ylabel("Hundreds of thousands", size=14)

plt.ylim([0, 6000])

plt.title("plot with Matplotlib")
Text(0.5, 1.0, 'plot with Matplotlib')

png

#6 Generate the histogram below of monthly passengers

Additional requirements:

  • Only include 1955 and beyond
  • Use a binwidth of 50, a min of 200, and a max of 700
  • Set the yticks to start at 0, end at 25 by interval of 5
temp=passengers[passengers['Year']>=1955].groupby(['Month'])['AirPassengers'].sum().reset_index()

plt.figure(figsize=(8,6))
plt.hist(temp['AirPassengers'],bins=10,range=(200,700))
plt.xlabel("Month", size=14)
plt.ylabel("Hundreds of thousands", size=14)

plt.title("plot with Matplotlib")
Text(0.5, 1.0, 'plot with Matplotlib')

png

#7 Generate the histogram below of monthly passengers

Additional requirements:

  • Generate two groups to compare. Group 1 should be the years 1949-1950. Group 2 should be the years 1959-60.
  • Binwidth of 50 from 100 to 700
  • yticks from 0 to 24, spaced by 2
  • Be sure to include a legend
temp1=passengers[ (passengers['Year']>=1949) & (passengers['Year']<=1950)].groupby(['Month'])['AirPassengers'].sum().reset_index()
temp2=passengers[ (passengers['Year']>=1959) & (passengers['Year']<=1960)].groupby(['Month'])['AirPassengers'].sum().reset_index()


plt.figure(figsize=(8,6))
plt.hist(temp1['AirPassengers'],bins=12,alpha=0.5,range=(100,700),label='1949-50')
plt.hist(temp2['AirPassengers'],bins=12,alpha=0.5,range=(100,700),label='1959-60')
plt.xlabel("Month", size=14)
plt.ylabel("Hundreds of thousands", size=14)

plt.yticks(np.arange(0, 24, 2.0))

plt.title("plot with Matplotlib")
plt.legend(loc='upper right')

<matplotlib.legend.Legend at 0x2d3705b9d90>

png

Author: robot learner
Link: https://datasciencebyexample.github.io/2021/08/25/2021-08-25-1/
Reprint policy: All articles in this blog are used except for special statements CC BY 4.0 reprint policy. If reproduced, please indicate source robot learner !
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