if type(i) == str:
try:
age.append(float(i))
except:
age.append(float(i[:-1]))
elif type(i) == float:
age.append(i)
elif type(i) == int:
age.append(i)
### Add new age column ###
df_vektis['AGE'] = age
### For getting some basic info ###
if input['check_missing'] == True:
func.check_missing(df, col, year)
if input['data_description'] == True:
func.data_describe(df, col, year)
### For three plots ###
loop = input['age_range']
for i in loop:
df_avg = func.groupAgeRange(df_vektis, i, 0)
if input['correlation_matrix'] == True:
func.corr_Matrix(df_avg, i, year)
if input['pie_chart'] == True:
func.pie_Chart(df_avg, i, year)
if input['distribution_plot'] == True:
for c in col:
if len(Counter(combined_df[c].dropna())) > 10:
numFea.append(c)
else:
catFea.append(c)
for i in range(0, len(input['taskName'])):
file = input['taskName'][i]
###############################
# 1.Overview on combined data #
###############################
### For getting some basic info ###
checkMissing = input['check_missing'][i]
if checkMissing == True:
func.check_missing(combined_df, col, file)
### Function for correlation matrix ###
CorrMatrix = input['correlation_matrix'][i]
if CorrMatrix == True:
func.corr_Matrix(combined_df[col], file)
### Function for Cat-Num plot ###
CN_plot = input["Cat_Num_plot"][i]
if CN_plot == True:
CN_feature = input["Cat_Num_feature"][i]
if len(CN_feature) > 0:
for f in CN_feature:
print(f)
func.plot_catNum(combined_df, f, file)
excluded_features = inputYAML['excluded_features']
if selected_features == "ALL":
if excluded_features == False:
col = df.columns
else:
col = df.drop(excluded_features, axis=1).columns
else:
col = selected_features
except:
logger.error("Some of your selected_features and excluded_features are not in the dataset")
else:
### Check missing values in the dataset ###
if inputYAML['check_missing'] == True:
func.check_missing(df, col, file_name)
### Get the basic description about the dataset ###
if inputYAML['data_description'] == True:
func.data_describe(df, col, file_name)
### Function for correlation matrix ###
if inputYAML['correlation_matrix'] == True:
func.corr_Matrix(df[col], file_name)
### Separate features to numerical and categorical ###
numFea = []
catFea = []
for c in col:
if len(Counter(df[c].dropna())) > 20:
numFea.append(c)