Future students of the course “Python Developer. Professional ” and everyone who is interested is invited to take part in the open webinar on the topic “ Frameworks and Metaclasses ”. And now we are sharing the traditional translation of useful material.
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Seaborn?
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pipenv install seaborn notebook
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import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib
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flights_data = sns.load_dataset("flights")
flights_data.head()
|
|
year |
month |
passengers |
|---|---|---|---|
0 |
1949 |
Jan |
112 |
1 |
1949 |
Feb |
118 |
2 |
1949 |
Mar |
132 |
3 |
1949 |
Apr |
129 |
4 |
1949 |
May |
121 |
load_dataset
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— Scatter Plot
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sns.scatterplot(data=flights_data, x="year", y="passengers")
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— Line Plot
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sns.lineplot(data=flights_data, x="year", y="passengers")
— Bar Plot
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sns.barplot(data=flights_data, x="year", y="passengers")
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matplotlib
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diamonds_data = sns.load_dataset('diamonds')
plt.subplot(1, 2, 1)
sns.countplot(x='carat', data=diamonds_data)
plt.subplot(1, 2, 2)
sns.countplot(x='depth', data=diamonds_data)
subplot
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drinks_df = pd.read_csv("data/drinks.csv")
sns.barplot(x="country", y="beer_servings", data=drinks_df)
Seaborn . : darkgrid, whitegrid, dark, white ticks.
sns.set_style("darkgrid")
sns.lineplot(data = data, x = "year", y = "passengers")
.
sns.set_style("whitegrid")
sns.lineplot(data=flights_data, x="year", y="passengers")
seaborn, . «tips», seaborn.
.
tips_df = sns.load_dataset('tips')
tips_df.head()
|
|
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 |
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tips_df["tip_percentage"] = tips_df["tip"] / tips_df["total_bill"]
tips_df.head()
:
|
|
total_bill |
tip |
sex |
smoker |
day |
time |
size |
tip_percentage |
|---|---|---|---|---|---|---|---|---|
0 |
16.99 |
1.01 |
Female |
No |
Sun |
Dinner |
2 |
0.059447 |
1 |
10.34 |
1.66 |
Male |
No |
Sun |
Dinner |
3 |
0.160542 |
2 |
21.01 |
3.50 |
Male |
No |
Sun |
Dinner |
3 |
0.166587 |
3 |
23.68 |
3.31 |
Male |
No |
Sun |
Dinner |
2 |
0.139780 |
4 |
24.59 |
3.61 |
Female |
No |
Sun |
Dinner |
4 |
0.146808 |
.
. histplot
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sns.histplot(tips_df["tip_percentage"], binwidth=0.05)
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sns.histplot(data=tips_df, x="tip_percentage", binwidth=0.05, hue="time")
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sns.barplot(data=tips_df, x="day", y="tip", estimator=np.sum)
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pivot = tips_df.pivot_table(
index=["day"],
columns=["size"],
values="tip_percentage",
aggfunc=np.average)
sns.heatmap(pivot)
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