Week 6

Combining dataframes

import pandas as pd
import numpy as np

rng = np.random.default_rng(10)

names = ["Ava", "Benjamin", "Charlotte", "Daniel", "Emma", "Fredric", "Gianna"]
courses = ["MTH 141", "MTH 142", "MTH 241", "MTH 306", "MTH 309", "MTH 311"]
rooms = ["NSC 216", "Capen 110", "Park 440"]

# concat rows data
scores1 = rng.integers(0, 100, 12).reshape(4, 3)
scores2 = rng.integers(0, 100, 9).reshape(3, 3)
columns = ["problem_1", "problem_2", "problem_3"]
sec1 = pd.DataFrame(scores1, index=names[:4], columns=columns)
sec2 = pd.DataFrame(scores2, index=names[4:7], columns=columns)

# concat columns data
scores1 = rng.integers(0, 100, 8).reshape(4, 2)
scores2 = rng.integers(0, 100, 9).reshape(3, 3)
part1 = pd.DataFrame(scores1,
                     index=names[:4],
                     columns=["problem_1", "problem_2"])
part2 = pd.DataFrame(scores2,
                     index=names[:3],
                     columns=["problem_3", "problem_4", "problem_5"])

# merging data
office_nums = rng.integers(100, 150, len(names[:-1]))
courses = pd.DataFrame({"course": courses,
                        "instructor": rng.choice(names[1:], len(courses))})
instructors = pd.DataFrame({"name": names[:-1], "office": office_nums}, dtype="object")

Concatenate along rows

display(sec1)
display(sec2)

	problem_1	problem_2	problem_3
Ava	77	95	26
Benjamin	20	79	82
Charlotte	51	14	83
Daniel	51	15	13

	problem_1	problem_2	problem_3
Emma	41	68	40
Fredric	84	0	42
Gianna	52	95	23

pd.concat([sec1, sec2])

	problem_1	problem_2	problem_3
Ava	77	95	26
Benjamin	20	79	82
Charlotte	51	14	83
Daniel	51	15	13
Emma	41	68	40
Fredric	84	0	42
Gianna	52	95	23

df = pd.concat([sec1, sec2], keys=["section_1", "section_2"])
df

		problem_1	problem_2	problem_3
section_1	Ava	77	95	26
	Benjamin	20	79	82
	Charlotte	51	14	83
	Daniel	51	15	13
section_2	Emma	41	68	40
	Fredric	84	0	42
	Gianna	52	95	23

df.index

MultiIndex([('section_1',       'Ava'),
            ('section_1',  'Benjamin'),
            ('section_1', 'Charlotte'),
            ('section_1',    'Daniel'),
            ('section_2',      'Emma'),
            ('section_2',   'Fredric'),
            ('section_2',    'Gianna')],
           )

df.loc[('section_1', 'Ava')]

problem_1    77
problem_2    95
problem_3    26
Name: (section_1, Ava), dtype: int64

df.loc['section_1']

	problem_1	problem_2	problem_3
Ava	77	95	26
Benjamin	20	79	82
Charlotte	51	14	83
Daniel	51	15	13

sec1 = sec1.rename({'problem_3': 'problem_3a'}, axis=1)
sec1

	problem_1	problem_2	problem_3a
Ava	77	95	26
Benjamin	20	79	82
Charlotte	51	14	83
Daniel	51	15	13

sec2 = sec2.rename({'problem_3': 'problem_3b'}, axis=1)
sec2

	problem_1	problem_2	problem_3b
Emma	41	68	40
Fredric	84	0	42
Gianna	52	95	23

pd.concat([sec1, sec2])

	problem_1	problem_2	problem_3a	problem_3b
Ava	77	95	26.0	NaN
Benjamin	20	79	82.0	NaN
Charlotte	51	14	83.0	NaN
Daniel	51	15	13.0	NaN
Emma	41	68	NaN	40.0
Fredric	84	0	NaN	42.0
Gianna	52	95	NaN	23.0

pd.concat([sec1, sec2], join='inner')

	problem_1	problem_2
Ava	77	95
Benjamin	20	79
Charlotte	51	14
Daniel	51	15
Emma	41	68
Fredric	84	0
Gianna	52	95

Concatenating along columns

display(part1)
display(part2)

	problem_1	problem_2
Ava	82	7
Benjamin	33	74
Charlotte	57	93
Daniel	75	91

	problem_3	problem_4	problem_5
Ava	82	13	93
Benjamin	84	14	97
Charlotte	74	31	13

pd.concat([part1, part2], axis=1)

	problem_1	problem_2	problem_3	problem_4	problem_5
Ava	82	7	82.0	13.0	93.0
Benjamin	33	74	84.0	14.0	97.0
Charlotte	57	93	74.0	31.0	13.0
Daniel	75	91	NaN	NaN	NaN

Merging

display(courses)
display(instructors)

	course	instructor
0	MTH 141	Emma
1	MTH 142	Charlotte
2	MTH 241	Benjamin
3	MTH 306	Gianna
4	MTH 309	Charlotte
5	MTH 311	Emma

	name	office
0	Ava	119
1	Benjamin	145
2	Charlotte	139
3	Daniel	111
4	Emma	124
5	Fredric	142

schedule = pd.merge(courses, 
                    instructors, 
                    left_on='instructor', 
                    right_on='name',
                    how='inner'
                   )
schedule

	course	instructor	name	office
0	MTH 141	Emma	Emma	124
1	MTH 311	Emma	Emma	124
2	MTH 142	Charlotte	Charlotte	139
3	MTH 309	Charlotte	Charlotte	139
4	MTH 241	Benjamin	Benjamin	145

schedule.drop("name", axis=1)

	course	instructor	office
0	MTH 141	Emma	124
1	MTH 311	Emma	124
2	MTH 142	Charlotte	139
3	MTH 309	Charlotte	139
4	MTH 241	Benjamin	145

schedule2 = pd.merge(courses, 
                    instructors, 
                    left_on='instructor', 
                    right_on='name',
                    how='left'
                   )
schedule2

	course	instructor	name	office
0	MTH 141	Emma	Emma	124
1	MTH 142	Charlotte	Charlotte	139
2	MTH 241	Benjamin	Benjamin	145
3	MTH 306	Gianna	NaN	NaN
4	MTH 309	Charlotte	Charlotte	139
5	MTH 311	Emma	Emma	124

schedule3 = pd.merge(courses, 
                    instructors, 
                    left_on='instructor', 
                    right_on='name',
                    how='right'
                   )
schedule3

	course	instructor	name	office
0	NaN	NaN	Ava	119
1	MTH 241	Benjamin	Benjamin	145
2	MTH 142	Charlotte	Charlotte	139
3	MTH 309	Charlotte	Charlotte	139
4	NaN	NaN	Daniel	111
5	MTH 141	Emma	Emma	124
6	MTH 311	Emma	Emma	124
7	NaN	NaN	Fredric	142

schedule4 = pd.merge(courses, 
                    instructors, 
                    left_on='instructor', 
                    right_on='name',
                    how='outer'
                   )
schedule4

	course	instructor	name	office
0	MTH 141	Emma	Emma	124
1	MTH 311	Emma	Emma	124
2	MTH 142	Charlotte	Charlotte	139
3	MTH 309	Charlotte	Charlotte	139
4	MTH 241	Benjamin	Benjamin	145
5	MTH 306	Gianna	NaN	NaN
6	NaN	NaN	Ava	119
7	NaN	NaN	Daniel	111
8	NaN	NaN	Fredric	142

Baby names project

pwd

'/Users/bb/Documents/GitHub/MTH548/preps/week_6'

ls

baby_names/             names.zip               week_6_prep.ipynb
baby_names.csv          namesbystate.zip        week_6_prep_2020.ipynb
my_plot.html            week_6.ipynb

from zipfile import ZipFile

with ZipFile("names.zip", 'r') as z:
    z.extractall(path='baby_names')

df = pd.read_csv("baby_names/yob1880.txt", names=["name", "sex", "count"])
df

	name	sex	count
0	Mary	F	7065
1	Anna	F	2604
2	Emma	F	2003
3	Elizabeth	F	1939
4	Minnie	F	1746
...	...	...	...
1995	Woodie	M	5
1996	Worthy	M	5
1997	Wright	M	5
1998	York	M	5
1999	Zachariah	M	5

2000 rows × 3 columns

import glob

file_names = glob.glob("baby_names/yob*.txt")
file_names

['baby_names/yob2000.txt',
 'baby_names/yob2014.txt',
 'baby_names/yob1938.txt',
 'baby_names/yob1910.txt',
 'baby_names/yob1904.txt',
 'baby_names/yob1905.txt',
 'baby_names/yob1911.txt',
 'baby_names/yob1939.txt',
 'baby_names/yob2015.txt',
 'baby_names/yob2001.txt',
 'baby_names/yob2017.txt',
 'baby_names/yob2003.txt',
 'baby_names/yob1907.txt',
 'baby_names/yob1913.txt',
 'baby_names/yob1898.txt',
 'baby_names/yob1899.txt',
 'baby_names/yob1912.txt',
 'baby_names/yob1906.txt',
 'baby_names/yob2002.txt',
 'baby_names/yob2016.txt',
 'baby_names/yob2012.txt',
 'baby_names/yob2006.txt',
 'baby_names/yob1902.txt',
 'baby_names/yob1916.txt',
 'baby_names/yob1889.txt',
 'baby_names/yob1888.txt',
 'baby_names/yob1917.txt',
 'baby_names/yob1903.txt',
 'baby_names/yob2007.txt',
 'baby_names/yob2013.txt',
 'baby_names/yob2005.txt',
 'baby_names/yob2011.txt',
 'baby_names/yob1915.txt',
 'baby_names/yob1901.txt',
 'baby_names/yob1929.txt',
 'baby_names/yob1928.txt',
 'baby_names/yob1900.txt',
 'baby_names/yob1914.txt',
 'baby_names/yob2010.txt',
 'baby_names/yob2004.txt',
 'baby_names/yob1973.txt',
 'baby_names/yob1967.txt',
 'baby_names/yob1998.txt',
 'baby_names/yob1999.txt',
 'baby_names/yob1966.txt',
 'baby_names/yob1972.txt',
 'baby_names/yob1958.txt',
 'baby_names/yob1964.txt',
 'baby_names/yob1970.txt',
 'baby_names/yob1971.txt',
 'baby_names/yob1965.txt',
 'baby_names/yob1959.txt',
 'baby_names/yob1961.txt',
 'baby_names/yob1975.txt',
 'baby_names/yob1949.txt',
 'baby_names/yob1948.txt',
 'baby_names/yob1974.txt',
 'baby_names/yob1960.txt',
 'baby_names/yob1976.txt',
 'baby_names/yob1962.txt',
 'baby_names/yob1989.txt',
 'baby_names/yob1988.txt',
 'baby_names/yob1963.txt',
 'baby_names/yob1977.txt',
 'baby_names/yob1952.txt',
 'baby_names/yob1946.txt',
 'baby_names/yob1991.txt',
 'baby_names/yob1985.txt',
 'baby_names/yob1984.txt',
 'baby_names/yob1990.txt',
 'baby_names/yob1947.txt',
 'baby_names/yob1953.txt',
 'baby_names/yob1979.txt',
 'baby_names/yob1945.txt',
 'baby_names/yob1951.txt',
 'baby_names/yob1986.txt',
 'baby_names/yob1992.txt',
 'baby_names/yob1993.txt',
 'baby_names/yob1987.txt',
 'baby_names/yob1950.txt',
 'baby_names/yob1944.txt',
 'baby_names/yob1978.txt',
 'baby_names/yob1940.txt',
 'baby_names/yob1954.txt',
 'baby_names/yob1968.txt',
 'baby_names/yob1983.txt',
 'baby_names/yob1997.txt',
 'baby_names/yob1996.txt',
 'baby_names/yob1982.txt',
 'baby_names/yob1969.txt',
 'baby_names/yob1955.txt',
 'baby_names/yob1941.txt',
 'baby_names/yob1957.txt',
 'baby_names/yob1943.txt',
 'baby_names/yob1994.txt',
 'baby_names/yob1980.txt',
 'baby_names/yob1981.txt',
 'baby_names/yob1995.txt',
 'baby_names/yob1942.txt',
 'baby_names/yob1956.txt',
 'baby_names/yob2009.txt',
 'baby_names/yob1919.txt',
 'baby_names/yob1931.txt',
 'baby_names/yob1925.txt',
 'baby_names/yob1886.txt',
 'baby_names/yob1892.txt',
 'baby_names/yob1893.txt',
 'baby_names/yob1887.txt',
 'baby_names/yob1924.txt',
 'baby_names/yob1930.txt',
 'baby_names/yob1918.txt',
 'baby_names/yob2020.txt',
 'baby_names/yob2008.txt',
 'baby_names/yob1926.txt',
 'baby_names/yob1932.txt',
 'baby_names/yob1891.txt',
 'baby_names/yob1885.txt',
 'baby_names/yob1884.txt',
 'baby_names/yob1890.txt',
 'baby_names/yob1933.txt',
 'baby_names/yob1927.txt',
 'baby_names/yob1923.txt',
 'baby_names/yob1937.txt',
 'baby_names/yob1894.txt',
 'baby_names/yob1880.txt',
 'baby_names/yob1881.txt',
 'baby_names/yob1895.txt',
 'baby_names/yob1936.txt',
 'baby_names/yob1922.txt',
 'baby_names/yob2018.txt',
 'baby_names/yob1934.txt',
 'baby_names/yob1920.txt',
 'baby_names/yob1908.txt',
 'baby_names/yob1883.txt',
 'baby_names/yob1897.txt',
 'baby_names/yob1896.txt',
 'baby_names/yob1882.txt',
 'baby_names/yob1909.txt',
 'baby_names/yob1921.txt',
 'baby_names/yob1935.txt',
 'baby_names/yob2019.txt']

frames = [pd.read_csv(f, names=["name", "sex", "count"]) for f in file_names]

frames[100]

	name	sex	count
0	Isabella	F	22311
1	Emma	F	17905
2	Olivia	F	17438
3	Sophia	F	16948
4	Ava	F	15873
...	...	...	...
34710	Zyquarius	M	5
34711	Zyran	M	5
34712	Zyreion	M	5
34713	Zyrian	M	5
34714	Zyvion	M	5

34715 rows × 3 columns

df = pd.concat(frames)
df

	name	sex	count
0	Emily	F	25957
1	Hannah	F	23084
2	Madison	F	19968
3	Ashley	F	17997
4	Sarah	F	17706
...	...	...	...
32025	Zyheem	M	5
32026	Zykel	M	5
32027	Zyking	M	5
32028	Zyn	M	5
32029	Zyran	M	5

2020863 rows × 3 columns

df[df['name'] == 'Emily']

	name	sex	count
0	Emily	F	25957
20595	Emily	M	30
6	Emily	F	12652
26398	Emily	M	11
163	Emily	F	1308
...	...	...	...
120	Emily	F	2184
9881	Emily	M	6
172	Emily	F	1094
11	Emily	F	8229
27045	Emily	M	8

218 rows × 3 columns

f = file_names[0]
f

'baby_names/yob2000.txt'

"hello.there".split('.')[0]

'hello'

int(f.split('.')[0][-4:])

2000

years = [int(f.split('.')[0][-4:]) for f in file_names]
years

[2000,
 2014,
 1938,
 1910,
 1904,
 1905,
 1911,
 1939,
 2015,
 2001,
 2017,
 2003,
 1907,
 1913,
 1898,
 1899,
 1912,
 1906,
 2002,
 2016,
 2012,
 2006,
 1902,
 1916,
 1889,
 1888,
 1917,
 1903,
 2007,
 2013,
 2005,
 2011,
 1915,
 1901,
 1929,
 1928,
 1900,
 1914,
 2010,
 2004,
 1973,
 1967,
 1998,
 1999,
 1966,
 1972,
 1958,
 1964,
 1970,
 1971,
 1965,
 1959,
 1961,
 1975,
 1949,
 1948,
 1974,
 1960,
 1976,
 1962,
 1989,
 1988,
 1963,
 1977,
 1952,
 1946,
 1991,
 1985,
 1984,
 1990,
 1947,
 1953,
 1979,
 1945,
 1951,
 1986,
 1992,
 1993,
 1987,
 1950,
 1944,
 1978,
 1940,
 1954,
 1968,
 1983,
 1997,
 1996,
 1982,
 1969,
 1955,
 1941,
 1957,
 1943,
 1994,
 1980,
 1981,
 1995,
 1942,
 1956,
 2009,
 1919,
 1931,
 1925,
 1886,
 1892,
 1893,
 1887,
 1924,
 1930,
 1918,
 2020,
 2008,
 1926,
 1932,
 1891,
 1885,
 1884,
 1890,
 1933,
 1927,
 1923,
 1937,
 1894,
 1880,
 1881,
 1895,
 1936,
 1922,
 2018,
 1934,
 1920,
 1908,
 1883,
 1897,
 1896,
 1882,
 1909,
 1921,
 1935,
 2019]

df = pd.concat(frames, keys=years)
df

		name	sex	count
2000	0	Emily	F	25957
	1	Hannah	F	23084
	2	Madison	F	19968
	3	Ashley	F	17997
	4	Sarah	F	17706
...	...	...	...	...
2019	32025	Zyheem	M	5
	32026	Zykel	M	5
	32027	Zyking	M	5
	32028	Zyn	M	5
	32029	Zyran	M	5

2020863 rows × 3 columns

df.loc[2020]

	name	sex	count
0	Olivia	F	17535
1	Emma	F	15581
2	Ava	F	13084
3	Charlotte	F	13003
4	Sophia	F	12976
...	...	...	...
31266	Zykell	M	5
31267	Zylus	M	5
31268	Zymari	M	5
31269	Zyn	M	5
31270	Zyran	M	5

31271 rows × 3 columns

df = df.reset_index(level=0)
df

	level_0	name	sex	count
0	2000	Emily	F	25957
1	2000	Hannah	F	23084
2	2000	Madison	F	19968
3	2000	Ashley	F	17997
4	2000	Sarah	F	17706
...	...	...	...	...
32025	2019	Zyheem	M	5
32026	2019	Zykel	M	5
32027	2019	Zyking	M	5
32028	2019	Zyn	M	5
32029	2019	Zyran	M	5

2020863 rows × 4 columns

df = df.rename({"level_0": "year"}, axis=1)
df

	year	name	sex	count
0	2000	Emily	F	25957
1	2000	Hannah	F	23084
2	2000	Madison	F	19968
3	2000	Ashley	F	17997
4	2000	Sarah	F	17706
...	...	...	...	...
32025	2019	Zyheem	M	5
32026	2019	Zykel	M	5
32027	2019	Zyking	M	5
32028	2019	Zyn	M	5
32029	2019	Zyran	M	5

2020863 rows × 4 columns

df = df.sort_values(by="year")
df

	year	name	sex	count
1638	1880	Dayton	M	8
1341	1880	Vern	M	19
1340	1880	Stewart	M	19
1339	1880	Randolph	M	19
1338	1880	Lucien	M	19
...	...	...	...	...
20853	2020	Hisham	M	31
20854	2020	Jahleel	M	31
20855	2020	Jameir	M	31
20857	2020	Jenesis	M	31
4287	2020	Medina	F	32

2020863 rows × 4 columns

df.to_csv("baby_names.csv", index=False)

df = pd.read_csv("baby_names.csv")

df.sort_values(by=['year', 'count'])

	year	name	sex	count
532	1880	Buck	M	5
533	1880	Brooks	M	5
534	1880	Bradley	M	5
535	1880	Bradford	M	5
536	1880	Brad	M	5
...	...	...	...	...
2016387	2020	Oliver	M	14147
2000004	2020	Emma	F	15581
2000003	2020	Olivia	F	17535
2016386	2020	Noah	M	18252
2016385	2020	Liam	M	19659

2020863 rows × 4 columns

Seaborn

import seaborn as sns
import matplotlib.pyplot as plt

tips = sns.load_dataset("tips")
tips

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

plt.scatter(tips["total_bill"], tips["tip"]);

With seaborn:

sns.relplot(data=tips,
            x="total_bill",
            y="tip"
);

sns.set_theme(style="darkgrid",
              palette="deep",
              font_scale=1.3
             )

sns.relplot(data=tips,
            x="total_bill",
            y="tip"
);

sns.relplot(data=tips,
            x="total_bill",
            y="tip",
            height=6,
            aspect=2
);

sns.relplot(data=tips,
            x="total_bill",
            y="tip",
            hue="day",
            height=6,
            aspect=2
);

sns.relplot(data=tips,
            x="total_bill",
            y="tip",
            hue="day",
            size="size",
            sizes=(5, 400),
            height=6,
            aspect=2
);

sns.relplot(data=tips,
            x="total_bill",
            y="tip",
            hue="day",
            size="size",
            col="time",
            sizes=(5, 400),
            height=10,
            aspect=1
);

sns.relplot(data=tips,
            x="total_bill",
            y="tip",
            hue="day",
            size="size",
            col="time",
            row="sex",
            sizes=(5, 400),
            height=10,
            aspect=1
);

Figure level vs axes-level functions

g = sns.jointplot(data=tips, x="total_bill", y="tip", height=8);
g.figure.suptitle("Restaurant data", y = 1.01)
g.set_axis_labels("Total bill", "Tips");

plt.figure(figsize=(14, 5))

plt.subplot(1,2,1)
sns.scatterplot(data=tips,
                x="total_bill",
                y="tip",
                hue="day"
               )
plt.plot([0, 50], [0, 10], 'b-', lw=5)

plt.subplot(1, 2, 2)
plt.plot([1, 2, 3, 4], [3, 2, 6, 1], 'ro-');

Plotly Express

%pip install plotly

Requirement already satisfied: plotly in /Users/bb/opt/anaconda3/lib/python3.8/site-packages (5.6.0)
Requirement already satisfied: six in /Users/bb/opt/anaconda3/lib/python3.8/site-packages (from plotly) (1.16.0)
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Note: you may need to restart the kernel to use updated packages.

import plotly.express as px
import plotly.io as pio

pio.renderers.default = "notebook"

fig = px.scatter(data_frame=tips,
                     x="total_bill",
                     y="tip",
                     title="Restaurant data",
                     height=400,
                     width=800
)

fig.show()

fig = px.scatter(data_frame=tips,
                     x="total_bill",
                     y="tip",
                     title="Restaurant data",
                     color="day"
)

fig.show()

fig = px.scatter(data_frame=tips,
                 x="total_bill",
                 y="tip",
                 title="Restaurant data",
                 color="day",
                 size="size"
                )

fig.show()

fig = px.scatter(data_frame=tips,
                 x="total_bill",
                 y="tip",
                 title="Restaurant data",
                 color="day",
                 size="size",
                 facet_col="time"
                )

fig.show()

fig = px.scatter(data_frame=tips,
                 x="total_bill",
                 y="tip",
                 title="Restaurant data",
                 color="day",
                 size="size",
                 facet_col="time",
                 facet_row="sex"
                )

fig.show()

fig.write_html("my_great_plot.html")

Plot types

Line plot

gapminder = px.data.gapminder()
gapminder

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4
...	...	...	...	...	...	...	...	...
1699	Zimbabwe	Africa	1987	62.351	9216418	706.157306	ZWE	716
1700	Zimbabwe	Africa	1992	60.377	10704340	693.420786	ZWE	716
1701	Zimbabwe	Africa	1997	46.809	11404948	792.449960	ZWE	716
1702	Zimbabwe	Africa	2002	39.989	11926563	672.038623	ZWE	716
1703	Zimbabwe	Africa	2007	43.487	12311143	469.709298	ZWE	716

1704 rows × 8 columns

ac = gapminder[gapminder["country"].str[0] == 'A' ]
ac

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4
...	...	...	...	...	...	...	...	...
79	Austria	Europe	1987	74.940	7578903	23687.826070	AUT	40
80	Austria	Europe	1992	76.040	7914969	27042.018680	AUT	40
81	Austria	Europe	1997	77.510	8069876	29095.920660	AUT	40
82	Austria	Europe	2002	78.980	8148312	32417.607690	AUT	40
83	Austria	Europe	2007	79.829	8199783	36126.492700	AUT	40

84 rows × 8 columns

fig = px.line(ac, 
              x="year",
              y="gdpPercap",
              color="country"
)

fig.show()

Bar plot

tips

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4
...	...	...	...	...	...	...	...
239	29.03	5.92	Male	No	Sat	Dinner	3
240	27.18	2.00	Female	Yes	Sat	Dinner	2
241	22.67	2.00	Male	Yes	Sat	Dinner	2
242	17.82	1.75	Male	No	Sat	Dinner	2
243	18.78	3.00	Female	No	Thur	Dinner	2

244 rows × 7 columns

fig = px.bar(tips,
             x="day",
             y="tip",
             color="sex",
             barmode="group",
             category_orders={"day": ["Thur", "Fri", "Sat", "Sun"]}, 
             title="Bar plot"
            )

fig.show()

Strip plot

fig = px.strip(tips, 
               x="day",
               y="tip",
               color="sex",
               title="Strip plot"

)

fig.show()

Box plot

fig = px.box(tips,
             x="day",
             y="tip",
             color="sex",
             category_orders={"day": ["Thur", "Fri", "Sat", "Sun"]}, 
             title="Box plot"
            )

fig.show()

Histogram

fig = px.histogram(tips,
                   x="total_bill",
                   nbins=6,
                   labels = {"total_bill": "total bill"},
                   title="Histogram"
)


fig.update_layout({"bargap": 0.02})
fig.show()

3D plot

fig = px.scatter_3d(tips, 
                    x="total_bill",
                    y="tip",
                    z="size",
                    color="day"
)

fig.show()