excel求变异系数,excel计算所有省份变异系数

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sns.distplot(a, bins=None, hist=True, kde=True)

该图的成绩分段使用系统默认的设置。结果整体成绩是否为“左偏”？确实是“左偏”。

# set bins fig,(ax1,ax2)= plt.subplots(1,2,sharex=True, figsize=(7,5)) plt.subplot(1,2,1) ax1 = sns.distplot(a=df['glkj'], bins=[10, 20, 30, 40, 50, 60, 70, 80, 90,100], norm_hist= False,hist=True, kde=False,label='管理会计成绩') ax1.set(title='19财管管理会计成绩',xlabel='管理会计成绩',ylabel='Count') ax1.legend(loc='best') #plt.tight_layout(rect=(1, 1, 1, 1)) #设置默认的间距 plt.subplot(1,2,2) ax2 = sns.distplot(a=df['glkj'], bins=[10, 20, 30, 40, 50, 60, 70, 80, 90,100], norm_hist= True,hist=True, kde=True,label='管理会计成绩KDE',color='green') ax2.set(title='19财管管理会计成绩KED',xlabel='管理会计成绩',ylabel='P') ax2.legend(loc='best') plt.subplots_adjust(wspace=0.3) plt.show()

bins = [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 101] labels = ['0-10','10-20','20-30','30-40','40-50','50-60','60-70','70-80','80-90','90 ']

• x:需要切分的数据
• bins:切分区域
• right : 是否包含右端点默认True，包含
• labels:对应标签，用标记来代替返回的bins，若不在该序列中，则返回NaN
• retbins:是否返回间距bins
• precision:精度
• include_lowest:是否包含左端点，默认False，不包含
• right : 是否包含右端点默认True，包含。该例为不包括False。[a,b)

df['glkj_bins'] = pd.cut(df['glkj'], bins=bins, labels=labels, include_lowest=True, right=False) class_count = df.groupby(by= 'class')['glkj_bins'].value_counts() pd_class_count= pd.DataFrame(class_count) pd_unstack = pd_class_count.unstack(fill_value=0)

for i in range(6): fig,(ax1,ax2)= plt.subplots(1,2,sharex=True,figsize=(8,6)) plt.subplot(1,2,1) ax1 = sns.barplot(count_bins,pd_unstack.values[i],label=pd_unstack.index[i]) ax1.legend(loc='best') ax1.set(xlabel= '管理会计分段成绩',ylabel= 'Count',title = '管理会计分班级成绩图') list_n = pd_unstack.values[i] for j, txt in enumerate(list_n): ax1.annotate(txt, (j, list_n[j] 0.6),horizontalalignment='center',verticalalignment='center') plt.subplot(1,2,2) ax2 = sns.distplot(a=df.loc[df['class']== pd_unstack.index[i]]['glkj'],bins=[10, 20, 30, 40, 50, 60, 70, 80, 90,100],norm_hist= True,hist=True, kde=True,label= pd_unstack.index[i],color='green') ax2.set(title='管理会计分班级成绩kde',xlabel='管理会计成绩',ylabel='P') ax2.legend(loc='best') plt.show()