Gráficas con seaborn

1 Traer el conjunto de datos tips

import plotly.express as px
tips = px.data.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

2 Usar pandas para el manejo de dataframes

import pandas as pd

3 Explorar un dataframe

tips.dtypes

total_bill    float64
tip           float64
sex            object
smoker         object
day            object
time           object
size            int64
dtype: object

tips.head

<bound method NDFrame.head of      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 x 7 columns]>

tips.tail

<bound method NDFrame.tail of      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 x 7 columns]>

tips.head(3)

	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

tips.index

RangeIndex(start=0, stop=244, step=1)

tips.describe()

	total_bill	tip	size
count	244.000000	244.000000	244.000000
mean	19.785943	2.998279	2.569672
std	8.902412	1.383638	0.951100
min	3.070000	1.000000	1.000000
25%	13.347500	2.000000	2.000000
50%	17.795000	2.900000	2.000000
75%	24.127500	3.562500	3.000000
max	50.810000	10.000000	6.000000

tips.columns

Index(['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size'], dtype='object')

tips.sort_values(by="tip")

	total_bill	tip	sex	smoker	day	time	size
67	3.07	1.00	Female	Yes	Sat	Dinner	1
236	12.60	1.00	Male	Yes	Sat	Dinner	2
92	5.75	1.00	Female	Yes	Fri	Dinner	2
111	7.25	1.00	Female	No	Sat	Dinner	1
0	16.99	1.01	Female	No	Sun	Dinner	2
...	...	...	...	...	...	...	...
141	34.30	6.70	Male	No	Thur	Lunch	6
59	48.27	6.73	Male	No	Sat	Dinner	4
23	39.42	7.58	Male	No	Sat	Dinner	4
212	48.33	9.00	Male	No	Sat	Dinner	4
170	50.81	10.00	Male	Yes	Sat	Dinner	3

244 rows × 7 columns

tips['smoker']

0       No
1       No
2       No
3       No
4       No
      ... 
239     No
240    Yes
241    Yes
242     No
243     No
Name: smoker, Length: 244, dtype: object

tips[0:3]

	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

4 Importar seaborn para graficar

Para más información vea seaborn y Waskom (2021) .

import seaborn as sns
import matplotlib.pyplot as plt 
sns.set_theme()

4.1 conjuntos de datos en seaborn

sns.get_dataset_names()

['anagrams',
 'anscombe',
 'attention',
 'brain_networks',
 'car_crashes',
 'diamonds',
 'dots',
 'dowjones',
 'exercise',
 'flights',
 'fmri',
 'geyser',
 'glue',
 'healthexp',
 'iris',
 'mpg',
 'penguins',
 'planets',
 'seaice',
 'taxis',
 'tips',
 'titanic']

4.1.1 Boxplot

sns.boxplot(x=tips["total_bill"])

4.1.2 Densidad con rug datos al eje

sns.kdeplot(data=tips, x="total_bill")
sns.rugplot(data=tips, x="total_bill")

4.1.3 Diagrama de dispersión

sns.relplot(
    data=tips,
    x="total_bill", y="tip", col="time",
    hue="smoker", style="smoker", size="size",
)

4.1.4 Histograma con densidad

sns.displot(data=tips, x="total_bill", col="time", kde=True)

4.1.5 Variable numérica y categórica

sns.catplot(data=tips, kind="swarm", x="day", y="total_bill", hue="smoker")

4.2 Conjunto de datos penguins

penguins = sns.load_dataset("penguins")
sns.jointplot(data=penguins, x="flipper_length_mm", y="bill_length_mm", hue="species")

4.2.1 Pares de variables

sns.pairplot(data=penguins, hue="species")    

4.2.2 Dispersión

sns.relplot(
    data=penguins,
    x="bill_length_mm", y="bill_depth_mm", hue="body_mass_g"
)

4.2.3 Histograma

sns.histplot(data=penguins, x="flipper_length_mm", hue="species", multiple="stack")

sns.displot(data=penguins, x="flipper_length_mm", hue="species", col="species")

sns.relplot(data=tips, x="total_bill", y="tip", hue="time", col="day", col_wrap=2)

Dispersión por hora y día

Referencias

Waskom, Michael L. 2021. «seaborn: statistical data visualization». Journal of Open Source Software 6 (60): 3021. https://doi.org/10.21105/joss.03021.