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pandas&matplotlib绘图基础

import matplotlib
pd.set_option("display.max_columns",100)
pd.set_option("display.max_rows",100)

matplotlib.rcParams
matplotlib.rcParams['font.sans-serif'] = ['SimHei']#显示中文
matplotlib.rcParams['axes.unicode_minus'] = False
from matplotlib.font_manager import _rebuild
_rebuild() #reload一下

1.箱线图

import matplotlib.pyplot as plt
%matplotlib inline 

df_2016.plot('population', kind='box')
df_2016.plot('gdp', kind='box')

def draw_boxplot(data):
    fig1, ax1 = plt.subplots()
    ax1.set_title('箱线图')
    ax1.boxplot(data,showbox=True,showcaps=True,meanline=True)

2.直方图

def draw_hist(data,bins,log):
    """
    绘制直方图
    data:必选参数，绘图数据
    bins:直方图的长条形数目，可选项，默认为10
    normed:是否将得到的直方图向量归一化，可选项，默认为0，代表不归一化，显示频数。normed=1，表示归一化，显示频率。
    facecolor:长条形的颜色
    edgecolor:长条形边框的颜色
    alpha:透明度
    """
    print('min:',np.percentile(data,0),'
'
          '25%:',np.percentile(data,25),'
'
          'median:',np.percentile(data,50),'
'
          '75%:',np.percentile(data,75),'
'
          'max:',np.percentile(data,100),'
'
         )
    plt.hist(data, bins, log = log ,density = 0,facecolor="blue", edgecolor="black", alpha=0.7)
    # 显示横轴标签
    plt.xlabel("区间")
    # 显示纵轴标签
    plt.ylabel("频数/频率")
    # 显示图标题
    plt.title("频数/频率分布直方图")
    plt.show()
    
    
data = np.random.randn(1000)*100
draw_hist(data,40,False)

3.柱状图

X=[0,1,2,3,4,5]
Y=[222,42,455,664,454,334]  
fig = plt.figure()
plt.bar(X,Y,0.4,color="green")

4.折线图

import matplotlib.pyplot as plt
%matplotlib inline

df_projects['totalamt'] = pd.to_numeric(df_projects['totalamt'].str.replace(',',''))

ax = df_projects.groupby('approvalyear')['totalamt'].sum()
.plot(x='approvalyear', y='totalamt',title ='Total Amount Approved per Year')

ax.set_xlabel('year')
ax.set_ylabel('amount #39;)
plt.show()

5.折线图：多系列

import matplotlib.pyplot as plt
%matplotlib inline

# put the data set into long form instead of wide
df_melt = pd.melt(df, id_vars=['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code'], var_name='year', value_name='GDP')

# convert year to a date time
df_melt['year'] = pd.to_datetime(df_melt['year'])

def plot_results(column_name):
    # plot the results for Afghanistan, Albania, and Honduras
    fig, ax = plt.subplots(figsize=(8,6))

    df_melt[(df_melt['Country Name'] == 'Afghanistan') | 
            (df_melt['Country Name'] == 'Albania') | 
            (df_melt['Country Name'] == 'Honduras')].groupby('Country Name').plot('year', column_name, legend=True, ax=ax)
    ax.legend(labels=['Afghanistan', 'Albania', 'Honduras'])
    
plot_results('GDP')

6.散点图

x = list(df_2016['population'])
y = list(df_2016['gdp'])
text = df_2016['Country Name']

fig, ax = plt.subplots(figsize=(15,10))
ax.scatter(x, y)
plt.title('GDP vs Population')
plt.xlabel('population')
plt.ylabel('GDP')

for i, txt in enumerate(text):
    ax.annotate(txt, (x[i],y[i]))

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