‘line’ : line plot (default)#折线图 ‘bar’ : vertical bar plot#条形图 ‘barh’ : horizontal bar plot#横向条形图 ‘hist’ : histogram#柱状图 ‘box’ : boxplot#箱线图 ‘kde’ : Kernel Density Estimation plot#Kernel 的密度估计图,主要对柱状图添加Kernel 概率密度线 ‘density’ : same as...
‘line’ : line plot (default)#折线图 ‘bar’ : vertical bar plot#条形图 ‘barh’ : horizontal bar plot#横向条形图 ‘hist’ : histogram#柱状图 ‘box’ : boxplot#箱线图 ‘kde’ : Kernel Density Estimation plot#Kernel 的密度估计图,主要对柱状图添加Kernel 概率密度线 ‘density’ : same as...
df.plot(kind='scatter', x='Temperature', y='Product_A_Sales', title='Product A Sales vs Temperature', figsize=(10, 6)); # 2-2 Histogram df['Temperature'].plot(kind='hist', bins=20, title='Temperature Distribution', figsize=(10, 6)); # 3-1 Stacked Area Chart df[['Product_A...
‘barh’ : horizontal bar plot#横向条形图 ‘hist’ : histogram#直方图(数值频率分布) ‘box’ : boxplot#箱型图 ‘kde’ : Kernel Density Estimation plot#密度图,主要对柱状图添加Kernel 概率密度线 ‘density’ : same as ‘kde’ ‘area’ : area plot#与x轴所围区域图(面积图)。Stacked=True时,...
The axes to plot the histogram on. 轴绘制直方图。 sharex: boolean, default True if ax is None else False In case subplots=True, share x axis and set some x axis labels to invisible; defaults to True if ax is None otherwise False if an ax is passed in. Note that passing in both ...
Axesindex: row labels;columns: column labels DataFrame.as_matrix([columns])转换为矩阵 DataFrame.dtypes返回数据的类型 DataFrame.ftypesReturn the ftypes (indication of sparse/dense and dtype) in this object. DataFrame.get_dtype_counts()返回数据框数据类型的个数 ...
If we want plot histogram on a specific column, then we can go with the column parameter of the hist()function. For, that we need to pass which column we want to plot the histogram into hist() function, it will plot the specified column histogram....
df['Marks].plot(kind='kde') # Example 2: Plot distribution of values in marks column # Using histogram df['Marks'].plot(kind='hist', edgecolor='black') # Example 3: Plot distribution of points by Students df.groupby('Students')['Marks'].plot(kind='kde') ...
But if you plot a histogram, too, you can also visualize the distribution of your data points. For this dataset above, a histogram would look like this: It’s very visual, very intuitive and tells you even more than the averages and variability measures above. I love it!
散点图Scatter plot DataFrame.boxplot([column, by, ax, …]) Make a box plot from DataFrame column optionally grouped by some columns or DataFrame.hist(data[, column, by, grid, …]) Draw histogram of the DataFrame’s series using matplotlib / pylab. ...