[ python  ]

Python

By Steve on July 29, 2019

Data Visualizations in Python and Jupyter Notebook Link to NB Viewer

import pandas as pd

from matplotlib import pyplot as plt

x = [1, 2, 3]
y = [1, 4, 9]
z = [10, 5, 0]
plt.plot(x, y)
plt.plot(x, z)
plt.title("Test Plot")
plt.xlabel("x")
plt.ylabel("y and z")
plt.legend(["This is Y", "This is Z"])
plt.show()

png

sample_data = pd.read_csv('sample_data.csv')

sample_data
column_a column_b column_c
0 1 1 10
1 2 4 8
2 3 9 6
3 4 16 4
4 5 25 2 
type(sample_data)

pandas.core.frame.DataFrame

sample_data.column_c

0    10
1     8
2     6
3     4
4     2
Name: column_c, dtype: int64

type(sample_data.column_c)

pandas.core.series.Series

sample_data.column_c.iloc[1]

8

plt.plot(sample_data.column_a, sample_data.column_b, 'o')
plt.plot(sample_data.column_a, sample_data.column_c)
plt.show()

png

data = pd.read_csv('countries.csv')

data
country year population
0 Afghanistan 1952 8425333
1 Afghanistan 1957 9240934
2 Afghanistan 1962 10267083
3 Afghanistan 1967 11537966
4 Afghanistan 1972 13079460
5 Afghanistan 1977 14880372
6 Afghanistan 1982 12881816
7 Afghanistan 1987 13867957
8 Afghanistan 1992 16317921
9 Afghanistan 1997 22227415
10 Afghanistan 2002 25268405
11 Afghanistan 2007 31889923
12 Albania 1952 1282697
13 Albania 1957 1476505
14 Albania 1962 1728137
15 Albania 1967 1984060
16 Albania 1972 2263554
17 Albania 1977 2509048
18 Albania 1982 2780097
19 Albania 1987 3075321
20 Albania 1992 3326498
21 Albania 1997 3428038
22 Albania 2002 3508512
23 Albania 2007 3600523
24 Algeria 1952 9279525
25 Algeria 1957 10270856
26 Algeria 1962 11000948
27 Algeria 1967 12760499
28 Algeria 1972 14760787
29 Algeria 1977 17152804
... ... ... ...
1674 Yemen, Rep. 1982 9657618
1675 Yemen, Rep. 1987 11219340
1676 Yemen, Rep. 1992 13367997
1677 Yemen, Rep. 1997 15826497
1678 Yemen, Rep. 2002 18701257
1679 Yemen, Rep. 2007 22211743
1680 Zambia 1952 2672000
1681 Zambia 1957 3016000
1682 Zambia 1962 3421000
1683 Zambia 1967 3900000
1684 Zambia 1972 4506497
1685 Zambia 1977 5216550
1686 Zambia 1982 6100407
1687 Zambia 1987 7272406
1688 Zambia 1992 8381163
1689 Zambia 1997 9417789
1690 Zambia 2002 10595811
1691 Zambia 2007 11746035
1692 Zimbabwe 1952 3080907
1693 Zimbabwe 1957 3646340
1694 Zimbabwe 1962 4277736
1695 Zimbabwe 1967 4995432
1696 Zimbabwe 1972 5861135
1697 Zimbabwe 1977 6642107
1698 Zimbabwe 1982 7636524
1699 Zimbabwe 1987 9216418
1700 Zimbabwe 1992 10704340
1701 Zimbabwe 1997 11404948
1702 Zimbabwe 2002 11926563
1703 Zimbabwe 2007 12311143

1704 rows × 3 columns

# Compare the population growth in the US and China

data[data.country == 'United States']
country year population
1608 United States 1952 157553000
1609 United States 1957 171984000
1610 United States 1962 186538000
1611 United States 1967 198712000
1612 United States 1972 209896000
1613 United States 1977 220239000
1614 United States 1982 232187835
1615 United States 1987 242803533
1616 United States 1992 256894189
1617 United States 1997 272911760
1618 United States 2002 287675526
1619 United States 2007 301139947 
us = data[data.country == 'United States']

china = data[data.country == 'China']

us
country year population
1608 United States 1952 157553000
1609 United States 1957 171984000
1610 United States 1962 186538000
1611 United States 1967 198712000
1612 United States 1972 209896000
1613 United States 1977 220239000
1614 United States 1982 232187835
1615 United States 1987 242803533
1616 United States 1992 256894189
1617 United States 1997 272911760
1618 United States 2002 287675526
1619 United States 2007 301139947 
china
country year population
288 China 1952 556263527
289 China 1957 637408000
290 China 1962 665770000
291 China 1967 754550000
292 China 1972 862030000
293 China 1977 943455000
294 China 1982 1000281000
295 China 1987 1084035000
296 China 1992 1164970000
297 China 1997 1230075000
298 China 2002 1280400000
299 China 2007 1318683096 
plt.plot(us.year, us.population / 10**6)
plt.plot(china.year, china.population / 10**6)
plt.legend(['United States', 'China'])
plt.xlabel('year')
plt.ylabel('population')
plt.show()

png

us.population

1608    157553000
1609    171984000
1610    186538000
1611    198712000
1612    209896000
1613    220239000
1614    232187835
1615    242803533
1616    256894189
1617    272911760
1618    287675526
1619    301139947
Name: population, dtype: int64

us.population / us.population.iloc[0] * 100

1608    100.000000
1609    109.159457
1610    118.396984
1611    126.123908
1612    133.222471
1613    139.787246
1614    147.371256
1615    154.109114
1616    163.052553
1617    173.219018
1618    182.589685
1619    191.135648
Name: population, dtype: float64

plt.plot(us.year, us.population / us.population.iloc[0] * 100)
plt.plot(china.year, china.population / china.population.iloc[0] * 100)
plt.legend(['United States', 'China'])
plt.xlabel('year')
plt.ylabel('population growth(first year = 100)')
plt.show()

png

plt.xkcd()
plt.plot(us.year, us.population / us.population.iloc[0] * 100)
plt.plot(china.year, china.population / china.population.iloc[0] * 100)
plt.legend(['United States', 'China'])
plt.xlabel('year')
plt.ylabel('population growth(first year = 100)')
plt.show()

png

df = pd.read_csv('pokemon_data.csv')

df_xlsx = pd.read_excel('pokemon_data')

# print(df.tail(3))


       #                 Name   Type 1 Type 2  HP  Attack  Defense  Sp. Atk  \
797  720  HoopaHoopa Confined  Psychic  Ghost  80     110       60      150   
798  720   HoopaHoopa Unbound  Psychic   Dark  80     160       60      170   
799  721            Volcanion     Fire  Water  80     110      120      130   

     Sp. Def  Speed  Generation  Legendary  
797      130     70           6       True  
798      130     80           6       True  
799       90     70           6       True  

# Read Headers 

# print(df.columns)

# Read each column 

# print(df['Name'])

# Read Each Row 

# print(df.head(4))

# print(df.iloc[5:14])

# df.loc[df['Type 1'] == "Fire"]

# df.loc[df['Type 1'] == "Grass"]

df.describe()
# HP Attack Defense Sp. Atk Sp. Def Speed Generation
count 800.000000 800.000000 800.000000 800.000000 800.000000 800.000000 800.000000 800.00000
mean 362.813750 69.258750 79.001250 73.842500 72.820000 71.902500 68.277500 3.32375
std 208.343798 25.534669 32.457366 31.183501 32.722294 27.828916 29.060474 1.66129
min 1.000000 1.000000 5.000000 5.000000 10.000000 20.000000 5.000000 1.00000
25% 184.750000 50.000000 55.000000 50.000000 49.750000 50.000000 45.000000 2.00000
50% 364.500000 65.000000 75.000000 70.000000 65.000000 70.000000 65.000000 3.00000
75% 539.250000 80.000000 100.000000 90.000000 95.000000 90.000000 90.000000 5.00000
max 721.000000 255.000000 190.000000 230.000000 194.000000 230.000000 180.000000 6.00000 
df.sort_values('Name', ascending = False)
# Name Type 1 Type 2 HP Attack Defense Sp. Atk Sp. Def Speed Generation Legendary
794 718 Zygarde50% Forme Dragon Ground 108 100 121 81 95 95 6 True
695 634 Zweilous Dark Dragon 72 85 70 65 70 58 5 False
46 41 Zubat Poison Flying 40 45 35 30 40 55 1 False
631 570 Zorua Dark NaN 40 65 40 80 40 65 5 False
632 571 Zoroark Dark NaN 60 105 60 120 60 105 5 False
286 263 Zigzagoon Normal NaN 38 30 41 30 41 60 3 False
707 644 Zekrom Dragon Electric 100 150 120 120 100 90 5 True
582 523 Zebstrika Electric NaN 75 100 63 80 63 116 5 False
157 145 Zapdos Electric Flying 90 90 85 125 90 100 1 True
367 335 Zangoose Normal NaN 73 115 60 60 60 90 3 False
793 717 Yveltal Dark Flying 126 131 95 131 98 99 6 True
520 469 Yanmega Bug Flying 86 76 86 116 56 95 4 False
208 193 Yanma Bug Flying 65 65 45 75 45 95 2 False
623 562 Yamask Ghost NaN 38 30 85 55 65 30 5 False
792 716 Xerneas Fairy NaN 126 131 95 131 98 99 6 True
192 178 Xatu Psychic Flying 65 75 70 95 70 95 2 False
394 360 Wynaut Psychic NaN 95 23 48 23 48 23 3 False
288 265 Wurmple Bug NaN 45 45 35 20 30 20 3 False
460 413 WormadamTrash Cloak Bug Steel 60 69 95 69 95 36 4 False
459 413 WormadamSandy Cloak Bug Ground 60 79 105 59 85 36 4 False
458 413 WormadamPlant Cloak Bug Grass 60 59 85 79 105 36 4 False
209 194 Wooper Water Ground 55 45 45 25 25 15 2 False
586 527 Woobat Psychic Flying 55 45 43 55 43 72 5 False
217 202 Wobbuffet Psychic NaN 190 33 58 33 58 33 2 False
301 278 Wingull Water Flying 40 30 30 55 30 85 3 False
45 40 Wigglytuff Normal Fairy 140 70 45 85 50 45 1 False
317 293 Whismur Normal NaN 64 51 23 51 23 28 3 False
372 340 Whiscash Water Ground 110 78 73 76 71 60 3 False
604 544 Whirlipede Bug Poison 40 55 99 40 79 47 5 False
607 547 Whimsicott Grass Fairy 60 67 85 77 75 116 5 False
... ... ... ... ... ... ... ... ... ... ... ... ...
182 168 Ariados Bug Poison 70 90 70 60 60 40 2 False
628 567 Archeops Rock Flying 75 140 65 112 65 110 5 False
627 566 Archen Rock Flying 55 112 45 74 45 70 5 False
552 493 Arceus Normal NaN 120 120 120 120 120 120 4 True
64 59 Arcanine Fire NaN 90 110 80 100 80 95 1 False
29 24 Arbok Poison NaN 60 85 69 65 79 80 1 False
379 347 Anorith Rock Bug 45 95 50 40 50 75 3 False
196 181 AmpharosMega Ampharos Electric Dragon 90 95 105 165 110 45 2 False
195 181 Ampharos Electric NaN 90 75 85 115 90 55 2 False
652 591 Amoonguss Grass Poison 114 85 70 85 80 30 5 False
471 424 Ambipom Normal NaN 75 100 66 60 66 115 4 False
768 698 Amaura Rock Ice 77 59 50 67 63 46 6 False
366 334 AltariaMega Altaria Dragon Fairy 75 110 110 110 105 80 3 False
365 334 Altaria Dragon Flying 75 70 90 70 105 80 3 False
655 594 Alomomola Water NaN 165 75 80 40 45 65 5 False
71 65 AlakazamMega Alakazam Psychic NaN 55 50 65 175 95 150 1 False
70 65 Alakazam Psychic NaN 55 50 45 135 95 120 1 False
205 190 Aipom Normal NaN 55 70 55 40 55 85 2 False
333 306 AggronMega Aggron Steel NaN 70 140 230 60 80 50 3 False
332 306 Aggron Steel Rock 70 110 180 60 60 50 3 False
154 142 AerodactylMega Aerodactyl Rock Flying 80 135 85 70 95 150 1 False
153 142 Aerodactyl Rock Flying 80 105 65 60 75 130 1 False
751 681 AegislashShield Forme Steel Ghost 60 50 150 50 150 60 6 False
750 681 AegislashBlade Forme Steel Ghost 60 150 50 150 50 60 6 False
678 617 Accelgor Bug NaN 80 70 40 100 60 145 5 False
393 359 AbsolMega Absol Dark NaN 65 150 60 115 60 115 3 False
392 359 Absol Dark NaN 65 130 60 75 60 75 3 False
68 63 Abra Psychic NaN 25 20 15 105 55 90 1 False
511 460 AbomasnowMega Abomasnow Grass Ice 90 132 105 132 105 30 4 False
510 460 Abomasnow Grass Ice 90 92 75 92 85 60 4 False

800 rows × 12 columns

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