How to write a loop random selected data and where the output is continuous data in python?

domarom 0
Viewed 28

I am trying to run the following code multiple times. Each time I run it I get different results because of the df.sample function. The output is a dataframe with 2 rows by 750 columns of continuous data because of the calculation loops seen near the end. How do I turn this into a loop so that I can run this code 100 times and save the dataframes output together? Any help would be very much appreciated! Thank you

df1 = dfs.sample(n=len(df_sample[df_sample.Column_name== 'Category']),replace=True)
df2 = dfs2.sample(n=len(df_sample[df_sample.Column_name== 'Category']),replace=True)
df3 = dfs3.sample(n=len(df_sample[df_sample.Column_name== 'Category']),replace=True)
all_dfs = [df1, df2, df3]
df4 = pd.concat(all_dfs, ignore_index=True)
#shp_gpd is a geopandas dataframe 
#creates a column to merge  df with geodataframe
df4['tmp'] = np.arange(len(df3))
shp_gpd['tmp'] = np.arange(len(shp_gpd))
df  = df_gpd.merge(df3, on = "tmp", how='left') 
gdf = gpd.GeoDataFrame(df)
    
#Calculations 
collist = list(gdf.columns)
emptydict = []
for j in collist[14:]:
    B1 = (((gdf.groupby(['Category1'])['Category2'].sum())/(gdf['Category2'].sum())) *  
            (gdf.groupby(['Category1'])[j].mean())).sum()
    res = {j:B1}
    emptydict.append(res)
resdf = pd.DataFrame(emptydict)
resdf1 = resdf.apply(lambda x: pd.Series(x.dropna().values))
resdf1

#standard deviation by for previous equation
collist1 = list(gdf.columns)
emptydict1 = []
for k in collist1[14:]:
    SD = (((gdf.groupby(['Category1'])['Category2'].sum())/(gdf['Category2'].sum())) * 
            (gdf.groupby(['Category1'])[k].std())).sum()
    res  = {k:SD}
    emptydict1.append(res)
SDdf = pd.DataFrame(emptydict1)
SDdf1 = SDdf.apply(lambda x: pd.Series(x.dropna().values))

df5 = [resdf1, SDdf1]
df6 = pd.concat(df5)
df6
#print(df6)
continuous append dataframe loops python
1 Answers

I turned it into a function and ran the loop on a simple function

for g in range(100):
    sample = random_sample()
    sample.to_csv(r'C:\Users\csv\out_{}.csv'.format(g))
domarom0
Related