Dynamic dataframe concatenation takes garbage value in python-flask - dataframe

Question

The below snippets takes list of features dynamically from the html form and the app.py computes the corresponding feature and append the selected feature all together and write it in a CSV file. The problem here is during concatenation, the dataframe of features which are not selected takes some garbage value during concatenation. Also suggest how to append the name of the feature to the header dynamically
<input type="checkbox" id="meanT" name="tdf" value="meanT">
<label for="mean"> Mean</label><br>
<input type="checkbox" id="stdT" name="tdf" value="stdT">
<label for="std"> Standard Deviation</label><br>
<input type="checkbox" id="medianT" name="tdf" value="medianT">
<label for="median"> Median</label><br>
<input type="checkbox" id="madT" name="tdf" value="madT">
<label for="mad"> Mean Absolute Deviation </label><br>
<input type="checkbox" id="rmsT" name="tdf" value="rmsT">
<label for="rms"> Root Mean Square</label><br>
<input type="checkbox" id="covT" name="tdf" value="covT">
<label for="cov"> Covariance</label><br>
app.py
#app.route('/feature_selection', methods =['GET', 'POST']) def feature_selection(): if request.method == 'POST': features=request.form.getlist('tdf')
`import os`
`ROOT_PATH = os.path.dirname(os.path.abspath(__file__))`
`files = request.files['fs_file']`
`files.save(os.path.join(ROOT_PATH,files.filename))
import pandas
raw_csv2 = pandas.read_csv(os.path.join(ROOT_PATH,files.filename))
X=raw_csv2.iloc[:,:-1]
print(X)
print(len(X.columns))
np.savetxt("D:/tool/feat_tobe_sel.csv",X,delimiter=',',fmt='%s')
from scipy.fftpack import fft
final=[]`
`final_mean = np.empty((1,len(X.columns)),np.float64)
final_std = np.empty((1,len(X.columns)),np.float64)
final_median = np.empty((1,len(X.columns)),np.float64)
final_mad = np.empty((1,len(X.columns)),np.float64)
final_rms = np.empty((1,len(X.columns)),np.float64)
final_cov = np.empty((1,len(X.columns)),np.float64)`
`for feature in features:
print(feature)
if feature=='meanT':
for chunk in pd.read_csv('D:/tool/feat_tobe_sel.csv',chunksize=250):
mean = np.array(chunk.mean())#mean
final_mean=np.append(final_mean,[mean],axis=0)
print("meanT")
elif feature=='stdT':
for chunk in pd.read_csv('D:/tool/feat_tobe_sel.csv',chunksize=250):
std = np.array(chunk.std())#standard deviation
final_std = np.append(final_std, [std], axis=0)
print("stdT")
elif feature=='medianT':
for chunk in pd.read_csv('D:/tool/feat_tobe_sel.csv',chunksize=250):
median = np.array(chunk.median())#median
final_median = np.append(final_median, [median], axis=0)
print("medianT")
elif feature=='madT':
for chunk in pd.read_csv('D:/tool/feat_tobe_sel.csv',chunksize=250):
mad = np.array(chunk.mad())
final_mad = np.append(final_mad, [mad], axis=0)
elif feature=='rmsT':
for chunk in pd.read_csv('D:/tool/feat_tobe_sel.csv',chunksize=250):
rms = np.array(np.sqrt(np.mean(chunk**2)))
final_rms = np.append(final_rms, [rms], axis=0)
print("rmsT")
elif feature=='covT':
for chunk in pd.read_csv('D:/tool/feat_tobe_sel.csv',chunksize=250):
cov = chunk.cov()
for covItem in cov:
final_cov = np.append(final_cov, [np.array(cov[covItem])], axis=0)`
`
`df2=pandas.DataFrame(final_mean)
df3=pandas.DataFrame(final_std)
df4=pandas.DataFrame(final_median)
df5=pandas.DataFrame(final_mad)
df6=pandas.DataFrame(final_rms)
df7=pandas.DataFrame(final_cov)
dfs = [df2,df3,df4,df5,df6,df7]`
`non_empty=[df for df in dfs if len(df)!=0]
dfm=pd.concat(non_empty,axis=1)
np.savetxt(r"D:/tool/features_selected.csv",dfm,delimiter=',',fmt='%s') `
`return rendertemplates("feat.html")`