数据科学导论——数据可视化

数据科学导论——数据可视化

第2关：初识数据

import pandas as pd
import numpy as np
pd.set_option(‘display.max_columns’, 1000)
pd.set_option(‘display.width’, 1000)
pd.set_option(‘display.max_colwidth’, 1000)
def student():

# ********* Begin *********#
df=pd.read_csv(“Task2/listings.csv”)
print(df.head(5))

# ********* End *********#

第3关：柱状图

import matplotlib
matplotlib.use(“Agg”)
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
def student():

# ********* Begin *********#
df=pd.read_csv(“Task3/listings.csv”)
plt.figure(figsize=(10,10))
sns.countplot(x = ‘room_type’,
data = df,
order = df[‘room_type’].value_counts(ascending=False).index)
plt.xticks(rotation=90)
plt.savefig(“Task3/img/T1.png”)
plt.show()

# ********* End *********#

第4关：散点图

import matplotlib
matplotlib.use(“Agg”)
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
def student():

# ********* Begin *********#
df=pd.read_csv(“Task4/listings.csv”)
plt.figure(figsize=(10,10))
sns.scatterplot(x=”longitude”,
y=”latitude”,
s=10,
data=df)
plt.savefig(“Task4/img/T1.png”)
plt.show()

# ********* End *********#

第5关：直方图

import matplotlib
matplotlib.use(“Agg”)
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings(‘ignore’)
def student(data,x,y):
”’
根据输入数据将直方图与线形图绘制在同一面板中
:param data: 绘制直方图数据，类型为list
:param x,y: 绘制线形图数据，类型为list
:return: None
”’
# ********* Begin *********#
fig = plt.figure(figsize=(10, 10))
sns.distplot(data,kde=False)
sns.lineplot(x,y)
plt.savefig(“Task5/img/T1.png”)