Creating a dictionary from specific rows of a .csv file-CodePudding

I want to create a dictionary with data from .csv file/files

I want to iterate through .csv files and use the first row of the .csv file as keys and third row of .csv file as values.

My code so far:

import pandas as pd
import collections
import operator
import functools
import os
import csv
import glob

root = "C:\\Users\\Public\\DiplomskaNaloga\\anomaly_count"

slovar_list = list()

for file in glob.glob(os.path.join(root, '*.csv')):
    df = pd.read_csv(file, encoding = 'cp1252')
    slovar = df.to_dict()
    slovar_list.append(slovar)

    res = dict(functools.reduce(operator.add,
                            map(collections.Counter, napake_list)))

I want a dictionary created as explained above. I will append each created dictionary to a list to create a list of dictionaries and then add the values of same keys as that is what I need as an output.

The command df.to_dict() doesn't work for me or I am just not experienced enough to understand the doc string on how to set it up to work for me

These zeros are from the second row which is empty. I dont need that.

CodePudding user response：

These zeros are NOT values from empty row(s) but indexes (number of row).

You should select single row using .iloc[] - but for your example output you should rather use index 0 instead of index 2 because it seems it skiped empty rows.

df.iloc[0].to_dict()

Minimal working example:

import pandas as pd

data = {
    'Column X': ['A','B','C'], 
    'Column Y': ['D','E','F'], 
    'Column Z': ['G','H','I']
}
df = pd.DataFrame(data)

print('--- df ---')
print(df)

print('--- all rows ---')
print(df.to_dict())

print('--- one row ---')
print(df.iloc[2].to_dict())

Result:

--- df ---
  Column X Column Y Column Z
0        A        D        G
1        B        E        H
2        C        F        I
--- all rows ---
{'Column X': {0: 'A', 1: 'B', 2: 'C'}, 'Column Y': {0: 'D', 1: 'E', 2: 'F'}, 'Column Z': {0: 'G', 1: 'H', 2: 'I'}}
--- one row ---
{'Column X': 'C', 'Column Y': 'F', 'Column Z': 'I'}