Python Data Visualizations

plt_example.py

# 图片 https://www.kaggle.com/benhamner/python-data-visualizations/notebook

# First, we'll import pandas, a data processing and CSV file I/O library
import pandas as pd

# We'll also import seaborn, a Python graphing library
import warnings # current version of seaborn generates a bunch of warnings that we'll ignore
warnings.filterwarnings("ignore")
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="white", color_codes=True)

# Next, we'll load the Iris flower dataset, which is in the "../input/" directory
iris = pd.read_csv("../input/Iris.csv") # the iris dataset is now a Pandas DataFrame

# Let's see what's in the iris data - Jupyter notebooks print the result of the last thing you do
print iris.head()

# Let's see how many examples we have of each species
print iris["Species"].value_counts()

# The first way we can plot things is using the .plot extension from Pandas dataframes
# We'll use this to make a scatterplot of the Iris features.
# 散点图
iris.plot(kind="scatter", x="SepalLengthCm", y="SepalWidthCm")

# One piece of information missing in the plots above is what species each plant is
# We'll use seaborn's FacetGrid to color the scatterplot by species
# 带颜色的散点图
sns.FacetGrid(iris, hue="Species", size=5) \
   .map(plt.scatter, "SepalLengthCm", "SepalWidthCm") \
   .add_legend()

# We can look at an individual feature in Seaborn through a boxplot
sns.boxplot(x="Species", y="PetalLengthCm", data=iris)

# Another useful seaborn plot is the pairplot, which shows the bivariate relation
# between each pair of features
# 
# From the pairplot, we'll see that the Iris-setosa species is separataed from the other
# two across all feature combinations
# feature两两组合
sns.pairplot(iris.drop("Id", axis=1), hue="Species", size=3)