Make it simpler, pandas multiple filer iteration - python-3.x

Question

import pandas as pd
import glob
import csv
files=glob.glob('*.csv')
for file in files:
df=pd.read_csv(file, header= None)
output_file_name = "output_" + file
with open(output_file_name, 'w') as f:
f.write("sum of the 1. column is " + str(df.iloc[:, 0].sum())+"\n")
f.write("sum of the 2. column is " + str(df.iloc[:, 1].sum())+"\n")
f.write("sum of the 3. column is " + str(df.iloc[:, 2].sum())+"\n")
f.write("sum of the 4. column is " + str(df.iloc[:, 3].sum())+"\n")
f.write("max of the 1. column is " + str(df.iloc[:, 0].max()) + "\n")
f.write("max of the 2. column is " + str(df.iloc[:, 1].max()) + "\n")
f.write("max of the 3. column is " + str(df.iloc[:, 2].max()) + "\n")
f.write("max of the 4. column is " + str(df.iloc[:, 3].max()) + "\n")
f.close()
How can iterate trough my pandas files, so that i dont have to repeat all this lines again. I want the same output file with this information about max and sum.
For every csv file i want a new file in the same folder that describe the max, sum , stdn etc. for example the output file will be:
sum of the 1. column is 21
sum of the 2. column is 23
sum of the 3. column is 33
sum of the 4. column is 30
max of the 1. column is 6
max of the 2. column is 6
max of the 3. column is 8
max of the 4. column is 9
How can it make simpler :D :D
Tnx

Answer 1

You could use a double for-loop to iterate over all the functions and columns:
for funcname in ['sum', 'max', 'std']:
for i in range(len(df.columns)):
f.write("sum of the {} column is {}\n"
.format(i+1, getattr(df.iloc[:, 0], funcname)()))
getattr(df, 'sum') is equivalent to df.sum.
import pandas as pd
import glob
import csv
files = glob.glob('*.csv')
for file in files:
df = pd.read_csv(file, header=None)
output_file_name = "output_" + file
with open(output_file_name, 'w') as f:
# f.write("{}\n".format(df.describe()))
for funcname in ['sum', 'max', 'std']:
for i in range(len(df.columns)):
f.write("sum of the {} column is {}\n"
.format(i+1, getattr(df.iloc[:, 0], funcname)()))
Note that df.describe() presents summary statistics in a neat format. You might want to consider just printing df.describe():
In [26]: df = pd.DataFrame(np.random.random((10,6)))
In [27]: df
Out[27]:
0 1 2 3 4 5
0 0.791727 0.397873 0.924195 0.202464 0.789961 0.077095
1 0.920516 0.637618 0.383694 0.623393 0.328440 0.606576
2 0.844562 0.231242 0.183842 0.902065 0.286643 0.743508
3 0.411101 0.370284 0.249545 0.955745 0.561450 0.597586
4 0.185035 0.989508 0.522821 0.218888 0.569865 0.773848
5 0.196904 0.377201 0.816561 0.914657 0.482806 0.686805
6 0.809536 0.480733 0.397394 0.152101 0.645284 0.921204
7 0.004433 0.168943 0.865408 0.472513 0.188554 0.012219
8 0.534432 0.739246 0.628112 0.789579 0.268880 0.835339
9 0.701573 0.580974 0.858254 0.461687 0.493617 0.285601
In [28]: df.describe()
Out[28]:
0 1 2 3 4 5
count 10.000000 10.000000 10.000000 10.000000 10.000000 10.000000
mean 0.539982 0.497362 0.582983 0.569309 0.461550 0.553978
std 0.324357 0.246491 0.274233 0.313254 0.189960 0.318598
min 0.004433 0.168943 0.183842 0.152101 0.188554 0.012219
25% 0.250453 0.372014 0.387119 0.279588 0.297092 0.363598
50% 0.618003 0.439303 0.575466 0.547953 0.488212 0.646691
75% 0.805084 0.623457 0.847830 0.873943 0.567761 0.766263
max 0.920516 0.989508 0.924195 0.955745 0.789961 0.921204

Answer 2

Use iloc for select first 4 columns, then apply function by agg, create columns starting with 1, reshape by stack, create list with list comprehension and last write to file by Series.to_csv:
files = glob.glob('*.csv')
for file in files:
df = pd.read_csv(file, header= None)
df1 = df.iloc[:, :4].agg(['sum','max','std'])
df1.columns = range(1, len(df1.columns) + 1)
s = df1.stack()
L = ['{} of the {}. column is {}'.format(a, b, c) for (a, b), c in s.items()]
output_file_name = "output_" + file
pd.Series(L).to_csv(output_file_name, index=False)