50个常用统计图表代码总结(10）
46. Seasonal Plot
The seasonal plot can be used to compare how the time series performed at same day in the previous season (year / month / week etc).
from dateutil.parser import parse
# Import Data
df = pd.read_csv('https://github.com/selva86/datasets/raw/master/AirPassengers.csv')
# Prepare data
df['year']=
df['month']=
years = df['year'].unique()
# Draw Plot
mycolors =['tab:red','tab:blue','tab:green','tab:orange','tab:brown','tab:grey','tab:pink','tab:olive','deeppink','steelblue','firebrick','mediumseagreen']
plt.figure(figsize=(16,10), dpi=80)
for i, y in enumerate(years):
plt.plot('month','traffic', data=df.loc, color=mycolors<i>, label=y)</i>
<i> plt.text(df.loc.shape-.9, df.loc[-1:].values, y, fontsize=12, color=mycolors)</i>
# Decoration
plt.ylim(50,750)
plt.xlim(-0.3,11)
plt.ylabel('$Air Traffic$')
plt.yticks(fontsize=12, alpha=.7)
plt.title("Monthly Seasonal Plot: Air Passengers Traffic (1949 - 1969)", fontsize=22)
plt.grid(axis='y', alpha=.3)
# Remove borders
plt.gca().spines["top"].set_alpha(0.0)
plt.gca().spines["bottom"].set_alpha(0.5)
plt.gca().spines["right"].set_alpha(0.0)
plt.gca().spines["left"].set_alpha(0.5)
# plt.legend(loc='upper right', ncol=2, fontsize=12)
plt.show()
Groups
47. Dendrogram
A Dendrogram groups similar points together based on a given distance metric and organizes them in tree like links based on the point’s similarity.
import scipy.cluster.hierarchy as shc
# Import Data
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/USArrests.csv')
# Plot
plt.figure(figsize=(16,10), dpi=80)
plt.title("USArrests Dendograms", fontsize=22)
dend = shc.dendrogram(shc.linkage(df[['Murder','Assault','UrbanPop','Rape']], method='ward'), labels=df.State.values, color_threshold=100)
plt.xticks(fontsize=12)
plt.show()
48. Cluster Plot
Cluster Plot canbe used to demarcate points that belong to the same cluster. Below is a representational example to group the US states into 5 groups based on the USArrests dataset. This cluster plot uses the ‘murder’ and ‘assault’ columns as X and Y axis. Alternately you can use the first to principal components as rthe X and Y axis.
from sklearn.cluster importAgglomerativeClustering
from scipy.spatial importConvexHull
# Import Data
df = pd.read_csv('https://raw.githubusercontent.com/selva86/datasets/master/USArrests.csv')
# Agglomerative Clustering
cluster =AgglomerativeClustering(n_clusters=5, affinity='euclidean', linkage='ward')
cluster.fit_predict(df[['Murder','Assault','UrbanPop','Rape']])
# Plot
plt.figure(figsize=(14,10), dpi=80)
plt.scatter(df.iloc[:,0], df.iloc[:,1], c=cluster.labels_, cmap='tab10')
# Encircle
def encircle(x,y, ax=None,**kw):
ifnot ax: ax=plt.gca()
p = np.c_
hull =ConvexHull(p)
poly = plt.Polygon(p,**kw)
ax.add_patch(poly)
# Draw polygon surrounding vertices
encircle(df.loc, df.loc, ec="k", fc="gold", alpha=0.2, linewidth=0)
encircle(df.loc, df.loc, ec="k", fc="tab:blue", alpha=0.2, linewidth=0)
encircle(df.loc, df.loc, ec="k", fc="tab:red", alpha=0.2, linewidth=0)
encircle(df.loc, df.loc, ec="k", fc="tab:green", alpha=0.2, linewidth=0)
encircle(df.loc, df.loc, ec="k", fc="tab:orange", alpha=0.2, linewidth=0)
# Decorations
plt.xlabel('Murder'); plt.xticks(fontsize=12)
plt.ylabel('Assault'); plt.yticks(fontsize=12)
plt.title('Agglomerative Clustering of USArrests (5 Groups)', fontsize=22)
plt.show()
49. Andrews Curve
Andrews Curve helps visualize if there are inherent groupings of the numerical features based on a given grouping. If the features (columns in the dataset) doesn’t help discriminate the group (cyl), then the lines will not be well segregated as you see below.
from pandas.plotting import andrews_curves
# Import
df = pd.read_csv("https://github.com/selva86/datasets/raw/master/mtcars.csv")
df.drop(['cars','carname'], axis=1, inplace=True)
# Plot
plt.figure(figsize=(12,9), dpi=80)
andrews_curves(df,'cyl', colormap='Set1')
# Lighten borders
plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(.3)
plt.title('Andrews Curves of mtcars', fontsize=22)
plt.xlim(-3,3)
plt.grid(alpha=0.3)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()
50. Parallel Coordinates
Parallel coordinates helps to visualize if a feature helps to segregate the groups effectively. If a segregation is effected, that feature is likely going to be very useful in predicting that group.
from pandas.plotting import parallel_coordinates
# Import Data
df_final = pd.read_csv("https://raw.githubusercontent.com/selva86/datasets/master/diamonds_filter.csv")
# Plot
plt.figure(figsize=(12,9), dpi=80)
parallel_coordinates(df_final,'cut', colormap='Dark2')
# Lighten borders
plt.gca().spines["top"].set_alpha(0)
plt.gca().spines["bottom"].set_alpha(.3)
plt.gca().spines["right"].set_alpha(0)
plt.gca().spines["left"].set_alpha(.3)
plt.title('Parallel Coordinated of Diamonds', fontsize=22)
plt.grid(alpha=0.3)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.show()
参考
https://www.machinelearningplus.com/plots/top-50-matplotlib-visualizations-the-master-plots-python/#3.-Scatter-plot-with-linear-regression-line-of-best-fit
文章来源于微信公众号：自学气象人
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