[seaborn] seaborn學習筆記12——繪圖實例(4) Drawing example(4)

12 繪圖實例(4) Drawing example(4)(代碼下載)

本文主要講述seaborn官網相關函數繪圖實例。具體內容有：

1. Scatterplot with varying point sizes and hues(relplot)
2. Scatterplot with categorical variables(swarmplot)
3. Scatterplot Matrix(pairplot)
4. Scatterplot with continuous hues and sizes(scatterplot)
5. Violinplots with observations(violinplot)
6. Discovering structure in heatmap data(clustermap)
7. Lineplot from a wide-form dataset(lineplot)
8. Violinplot from a wide-form dataset(violinplot)

# import packages
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

1. Scatterplot with varying point sizes and hues(relplot)

sns.set(style="white")

# Load the example mpg dataset
mpg = sns.load_dataset("mpg")

# Plot miles per gallon against horsepower with other semantics
# 其中x,y爲橫軸座標變量，hue表示分類類別，size表示點的大小
sns.relplot(x="horsepower", y="mpg", hue="origin", size="weight",
            sizes=(40, 400), alpha=.5, palette="muted",
            height=6, data=mpg);

2. Scatterplot with categorical variables(swarmplot)

# Load the example iris dataset
iris = sns.load_dataset("iris")

# "Melt" the dataset to "long-form" or "tidy" representation
# 合併數據集
iris = pd.melt(iris, "species", var_name="measurement")

# Draw a categorical scatterplot to show each observation
# swarmplot將不同類別散點圖用樹狀表示
sns.swarmplot(x="measurement", y="value", hue="species",
              palette=["r", "c", "y"], data=iris);

3. Scatterplot Matrix(pairplot)

df = sns.load_dataset("iris")
#製作多變量圖，hue爲使用指定變量爲分類變量畫圖
sns.pairplot(df, hue="species");

C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval

4. Scatterplot with continuous hues and sizes(scatterplot)

# Load the example iris dataset
planets = sns.load_dataset("planets")
# 設定顏色
#cubehelix_palette表示從cubehelix中製作順序調色板
cmap = sns.cubehelix_palette(rot=-.2, as_cmap=True)
ax = sns.scatterplot(x="distance", y="orbital_period",
                     hue="year", size="mass",
                     palette=cmap, sizes=(10, 200),
                     data=planets)

5. Violinplots with observations(violinplot)

# Create a random dataset across several variables
rs = np.random.RandomState(0)
n, p = 40, 8
d = rs.normal(0, 2, (n, p))
d += np.log(np.arange(1, p + 1)) * -5 + 10

# Use cubehelix to get a custom sequential palette
pal = sns.cubehelix_palette(p, rot=-.5, dark=.3)

# Show each distribution with both violins and points
# 製作小提琴圖，pal表示顏色
sns.violinplot(data=d, palette=pal, inner="points");

6. Discovering structure in heatmap data(clustermap)

# Load the brain networks example dataset
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)

# Select a subset of the networks
used_networks = [1, 5, 6, 7, 8, 12, 13, 17]
used_columns = (df.columns.get_level_values("network")
                          .astype(int)
                          .isin(used_networks))
# 建立矩陣類數據集
df = df.loc[:, used_columns]

# Create a categorical palette to identify the networks
#創建調色盤
network_pal = sns.husl_palette(8, s=.45)
network_lut = dict(zip(map(str, used_networks), network_pal))

# Convert the palette to vectors that will be drawn on the side of the matrix
networks = df.columns.get_level_values("network")
network_colors = pd.Series(networks, index=df.columns).map(network_lut)

# Draw the full plot
# 將矩陣數據集繪製爲分層聚類熱圖
# row_colors,col_color行或列標記的顏色列表
sns.clustermap(df.corr(), center=0, cmap="vlag",
               row_colors=network_colors, col_colors=network_colors,
               linewidths=.75, figsize=(13, 13));

7. Lineplot from a wide-form dataset(lineplot)

sns.set(style="whitegrid")

rs = np.random.RandomState(365)
values = rs.randn(365, 4).cumsum(axis=0)
dates = pd.date_range("1 1 2016", periods=365, freq="D")
data = pd.DataFrame(values, dates, columns=["A", "B", "C", "D"])
data = data.rolling(7).mean()

# 創建折線圖
sns.lineplot(data=data, palette="tab10", linewidth=2.5);

8. Violinplot from a wide-form dataset(violinplot)

# Load the example dataset of brain network correlations
df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)

# Pull out a specific subset of networks
used_networks = [1, 3, 4, 5, 6, 7, 8, 11, 12, 13, 16, 17]
used_columns = (df.columns.get_level_values("network")
                          .astype(int)
                          .isin(used_networks))
#創建矩陣
df = df.loc[:, used_columns]

# Compute the correlation matrix and average over networks
# 計算相對係數和均值
corr_df = df.corr().groupby(level="network").mean()
corr_df.index = corr_df.index.astype(int)
corr_df = corr_df.sort_index().T

# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(11, 6))

# Draw a violinplot with a narrower bandwidth than the default
sns.violinplot(data=corr_df, palette="Set3", bw=.2, cut=1, linewidth=1)

# Finalize the figure
ax.set(ylim=(-.7, 1.05))
sns.despine(left=True, bottom=True);