def run(self, df, x, y, extra_args={'bins': 10}):
#msg={'error':None,'warning':None}
tsy = df[x]
tsx = df[y]
tsy = tsy.reset_index(drop=True)
tsx = tsx.reset_index(drop=True)
msg = {}
xl = len(tsx)
yl = len(tsy)
if xl != yl:
msg['error'] = '输入的tsx的长度为:%s 不等于输入的tsy的长度: %s !\n ' % (xl, yl)
return {'result': pd.DataFrame(), 'msg': msg}
self.bins = extra_args.get('bins')
if not isSeries(tsy) & isSeries(tsx):
msg['error'] = 'tsx或者tsy不是 pandas Series 数据类型!\n'
return {'result': pd.DataFrame(), 'msg': msg}
else:
if not isCategory(tsy):
tsy = pd.cut(tsy, bins=self.bins)
msg['warning'] = '列tsy不是定类（category）数据, 将强制通过bins:%d为转化为定类型数据\n' % self.bins
if not isCategory(tsx):
tsx = pd.cut(tsx, bins=self.bins)
if msg.get('warning'):
msg['warning'] = msg[
'warning'] + '列tsx不是定类（category）数据, 将强制通过bins:%d为转化为定类型数据\n' % self.bins
else:
msg['warning'] = 't列tsx不是定类（category）数据, 将强制通过bins:%d为转化为定类型数据\n' % self.bins
dfres, msg1 = core(tsx, tsy, method)
msg = {**msg, **msg1}
return {
'tables': [{
'table_json':
dfres.T.reset_index().to_json(orient='index'),
'table_html':
dfres.to_html(),
'table_info':
'卡方检验分析结果',
'chart': ['heatmap', 'line', 'bar']
}],
'conf':
self.get_info(),
'msg':
msg
}, [{
'table_df': dfres,
'label': '卡方检验分析结果'
}]
def run(self, tsx, tsy, bins=10):
msg = {}
tsy = tsy.reset_index(drop=True)
tsx = tsx.reset_index(drop=True)
msg = {}
xl = len(tsx)
yl = len(tsy)
if xl != yl:
logging.error(
'the length of input X:%s is not equal the length of Y: %s ! '
% (xl, yl))
msg['error'] = '输入的tsx的长度为:%s 不等于输入的tsy的长度: %s ! ' % (xl, yl)
return {'result': pd.DataFrame(), 'msg': msg}
self.bins = bins
if not isSeries(tsy) & isSeries(tsx):
logging.error('X or y data are not a pandas Series type!')
msg['error'] = 'tsx或者tsy不是 pandas Series 数据类型!'
return {'result': pd.DataFrame(), 'msg': msg}
else:
if not isCategory(tsy):
tsy = pd.cut(tsy, bins=bins)
logging.warning(
'the Series tsy is not category type, will be convert to category type by bins of %d'
% self.bins)
msg['warning'] = '列tsy不是定类（category）数据, 将强制通过bins:%d为转化为定类型数据' % self.bins
if not isCategory(tsx):
tsx = pd.cut(tsx, bins=bins)
logging.warning(
'the Series tsx is not category type, will be convert to category type by bins of %d'
% self.bins)
if msg.get('warning'):
msg['warning'] = msg[
'warning'] + '列tsx不是定类（category）数据, 将强制通过bins:%d为转化为定类型数据' % self.bins
else:
msg['warning'] = 't列tsx不是定类（category）数据, 将强制通过bins:%d为转化为定类型数据' % self.bins
table, results = crosstab(tsx, tsy, prop='col', test='chi-square')
return {'result': results, 'table': table, 'msg': msg}
def run(self, dfx, tsy):
tsy = tsy.reset_index(drop=True)
dfx = dfx.reset_index(drop=True)
msg = {}
xl = len(dfx)
yl = len(tsy)
if xl != yl:
logging.error(
'the length of input X:%s is not equal the length of Y: %s ! '
% (xl, yl))
msg['error'] = '输入的dfx的长度为:%s 不等于输入的tsy的长度: %s ' % (xl, yl)
return {'result': pd.DataFrame(), 'msg': msg}
if not isSeries(tsy) or not isCategory(tsy):
logging.error(
'input tsy is not a pandas Series or not a category data!')
msg['error'] = '输入的tsy不是定类型数据或者Series类型'
return {'result': pd.DataFrame(), 'msg': msg}
else:
x_numer_cols, x_cate_cols = ParseDFtypes(dfx)
if x_numer_cols == []:
logging.error(
'All input dfx are no numeric columns, Please check your input dfx data!'
)
msg['error'] = '输入的dfx所有的列都不是数值型数据，请检查输入数据'
return {'result': pd.DataFrame(), 'msg': msg}
else:
if x_cate_cols != []:
logging.warning(
'input dfx has non-numeric columns: %s, will ignore these columns!'
% x_cate_cols)
msg['warning'] = '输入的dfx包含了非数值型的列: %s, 将会被自动忽略！' % x_cate_cols
name = tsy.name
dfu = dfx[x_numer_cols].join(tsy)
m = dfu.groupby(name).mean().T
s = dfu.groupby(name).std().T
def change(ts):
v = []
for i in ts.index:
r = '%s±%s' % (round(ts.loc[i],
2), round(s[ts.name].loc[i], 2))
v.append(r)
return pd.Series(v, index=ts.index)
m1 = m.apply(change)
rs = []
for i in x_numer_cols:
model = ols('%s ~ %s' % (i, tsy.name), dfu).fit()
anovat = anova_lm(model)
anovat.columns = ['自由度', '平方和', '均方和', 'F-值', 'p-值']
rs.append(anovat.iloc[0].to_frame(name=i).T)
res = m1.join(pd.concat(rs))
res['p-值'] = res['p-值'].apply(lambda x: '{:.5f}'.format(x))
return {'result': res.round(5), 'msg': msg}
def run(self, dfx, tsy):
tsy = tsy.reset_index(drop=True)
dfx = dfx.reset_index(drop=True)
msg = {}
xl = len(dfx)
yl = len(tsy)
if xl != yl:
logging.error(
'the length of input X:%s is not equal the length of Y: %s ! '
% (xl, yl))
msg['error'] = 'the length of input X:%s is not equal the length of Y: %s ! ' % (
xl, yl)
return {'result': pd.DataFrame(), 'msg': msg}
if not isSeries(tsy) or not isCategory(tsy):
logging.error(
'input tsy is not a pandas Series or not a category data!')
msg['error'] = 'input tsy is not a pandas Series or not a category data!'
return {'result': pd.DataFrame(), 'msg': msg}
else:
x_numer_cols, x_cate_cols = ParseDFtypes(dfx)
if x_numer_cols == []:
logging.error(
'All input dfx are no numeric columns, Please check your input dfx data!'
)
msg['error'] = 'All input dfx are no numeric columns, Please check your input dfx data!'
return {'result': pd.DataFrame(), 'msg': msg}
else:
if x_cate_cols != []:
logging.warning(
'input dfx has non-numeric columns: %s, will ignore these columns!'
% x_cate_cols)
msg['warning'] = 'input dfx has non-numeric columns: %s, will ignore these columns!' % x_cate_cols
name = tsy.name
dfu = dfx[x_numer_cols].join(tsy)
m = dfu.groupby(name).mean().T
s = dfu.groupby(name).std().T
def change(ts):
v = []
for i in ts.index:
r = '%s±%s' % (round(ts.loc[i],
2), round(s[ts.name].loc[i], 2))
v.append(r)
return pd.Series(v, index=ts.index)
m1 = m.apply(change)
rs = []
for i in x_numer_cols:
dd = [dfu[dfu[tsy.name] == c][i] for c in tsy.unique()]
F, p = levene(*dd)
columns = ['F-值', 'p-值']
rs.append(
pd.DataFrame([F, p], index=columns, columns=[i]).T)
res = m1.join(pd.concat(rs))
return {'result': res, 'msg': msg}
def run(self,
dfx,
tsy):
tsy = tsy.reset_index(drop=True)
dfx = dfx.reset_index(drop=True)
msg = {}
if not isSeries(tsy) or not isCategory(tsy):
logging.error('input tsy is not a pandas Series or not a category data!')
msg['error'] = '输入的tsy不是定类型数据或者Series类型'
return {'result':pd.DataFrame(), 'msg':msg}
else:
if len(tsy.unique()) != 2:
msg['error'] = '输入的tsy不能被分成2组，请确保值tsy中的数unique后元素个数为2，目前的元素为%s' % tsy.unique()
return {'result':pd.DataFrame(), 'msg':msg}
else:
x_numer_cols, x_cate_cols = ParseDFtypes(dfx)
if x_numer_cols ==[]:
logging.error('All input dfx are no numeric columns, Please check your input dfx data!')
msg['error'] = 'dfx输入的每列都不是数值型数据，请检查输入数据'
return {'result':pd.DataFrame(), 'msg':msg}
else:
if x_cate_cols != []:
logging.warning('input dfx has non-numeric columns: %s, will ignore these columns!' % x_cate_cols)
msg['warning'] = '输入的dfx包含了非数值型的列: %s, 将会被自动忽略！' % x_cate_cols
name = tsy.name
dfu = dfx[x_numer_cols].join(tsy)
m = dfu.groupby(name).mean().T
s = dfu.groupby(name).std().T
def change(ts):
v= []
for i in ts.index:
r = '%s±%s' % (round(ts.loc[i],2),round(s[ts.name].loc[i],2))
v.append(r)
return pd.Series(v,index=ts.index)
m1 = m.apply(change)
rs = []
for i in x_numer_cols:
c1 = tsy.unique()[0]
c2 = tsy.unique()[1]
d1 = dfu[dfu[tsy.name] == c1][i]
d2 = dfu[dfu[tsy.name] == c2][i]
F, p = ttest_ind(d1,d2)
columns = ['t-值', 'p-值']
rs.append(pd.DataFrame([F,p],index=columns,columns=[i]).T)
res = m1.join(pd.concat(rs))
return {'result':res.round(5), 'msg':msg}