1.导入数据 a)导入.xlsx 格式excel read_excel( ) 导入指定sheet: 设定sheet_name参数,来指定要导入哪个Sheet的文件。 行索引index_col:表示用.xlsx文件中的第几列做行索引,从0开始计数。 列索引header:将本地文件导入DataFrame时,默认使用源数据表第一行作为列索引,也可以通过设置header参数来设置列索引 指定导...
df['r'] = some_expression # add a (virtual) column that will be computed on the fly df.mean(df.x), df.mean(df.r) # calculate statistics on normal and virtual columns 可视化方法也是: df.plot(df.x, df.y, show=True); # make a plot quickly 它的官方提供一个例子,就是纽约市出租车...
0,0)写一个自动化的小脚本deff():sht_3.range("A1:AZ48").column_width=1.1sht_3.range(...
plt.show() 可视化为饼状图 代码语言:javascript 代码运行次数:0 运行 AI代码解释 var=df.groupby(['Gender']).sum().stack()temp=var.unstack()type(temp)x_list=temp['Sales']label_list=temp.index pyplot.axis("equal")#The pie chart is oval bydefault.To make it a circle use pyplot.axis("e...
a Nx1 column vector (needed by CVXOPT)self.y = y.reshape(-1,1).astype(np.double)# Has to be a column vectorself.X = XN = X.shape[0]# Number of points# compute the kernel over all possible pairs of (x, x') in the data# by Numpy's v...
#Create a function to build a regression model with parameterized degree of independent coefficientsdefcreate_model(x_train,degree): degree+=1X_train = np.column_stack([np.power(x_train,i)foriinrange(0,degree)]) model = np.dot(np.dot(np.linalg.inv(np.dot(X_train.transpose(),X_train...
# make data np.random.seed(1)x=4+np.random.normal(0,1.5,200)#画直方图hist plt.hist(x)plt.show() 复制 2、Seaborn Seaborn 是一个基于 matplotlib 的可视化库。它的特点是可以用简洁的代码画出复杂好看的图表! 3、Plotly Plotly是一个开源,交互式和基于浏览器的Python图形库,它的特点是可以创建互动性...
help='Set job parameter, eg: the source tableName you want to set it by command,''then you can use like this: -p"-DtableName=your-table-name",''if you have mutiple parameters: -p"-DtableName=your-table-name -DcolumnName=your-column-name".''Note: you should config in you job ...
df = pd.read_sql(sql=sql,con=con,index_col=True,coerce_float=True,columns=True) 或者: df = pd.read_sql_table(tablename,con=con,index_col=True,coerce_float=True,columns=True) # sql--查询语句 ,con--数据库连接信息(地址,用户名,密码,数据库名称) ...
完整的代码如下：# Calculate the Gini index for a split datasetdef gini_index(groups, class_values):gini = 0.0for class_value in class_values:for group in groups:size = len(group)if size == 0:continueproportion = [row[-1] for row in group].count(class_value) / float(size)gini +=...