You can do this very simply using pandas.

import pandas as pd

# get only the columns you want from the csv file
df = pd.read_csv(target_path + target_file, usecols=['Column Name1', 'Column Name2'])
result = df.to_dict(orient='records')

Sources:

• pandas.read_csv
• pandas.DataFrame.to_dict

Pandas has some learning curve, but once you are comfortable with them, you will love them. See one simple solution to your problem. Cheers.

import pandas as pd
import matplotlib.pyplot as plt
from datetime import timedelta

df = pd.read_csv(r"your-folder\dates.csv", parse_dates=[0]) # assuming dates are in the first column
df.sort_values(by="date", ascending=False, inplace=True)
last_date = df["date"].max().date()
start_date = df["date"].max().date() - timedelta(days=365)
dfRange = df[(df["date"].dt.date >= start_date) & (df["date"].dt.date <= last_date)]

plt.scatter(dfRange["txCount"],dfRange["txVolume(USD)"])
plt.xlabel("txCount")
plt.ylabel("txVolume(USD)")
plt.savefig(r"target-location\dates.png")
plt.close()

unicodecsv library can also be used to read .csv files.

import unicodecsv
import pandas as pd

def read_csv(data):
""" Returns a list of dicts from .csv file passed in as data"""
    with open(data, "rb") as f:
        reader = list(unicodecsv.DictReader(f))
        return reader

file = read_csv('filename.csv') # call the function and pass in your 'filename'

pd.DataFrame(file) # call pd.DataFrame() function with file as an argument 
                   # to convert it to DataFrame object

csv.reader

Given your desired output, you do not need to use dict or, consequently, csv.DictReader. Instead, just use csv.reader, which returns an iterator. Then use next and list to extract headers and data respectively:

from io import StringIO
import csv

x = StringIO("""play,weather,temperature
yes,sunny,77
no,rainy,60
yes,windy,70""")

# replace x with open('file.csv', 'r')
with x as fin:
    reader = csv.reader(fin)
    headers = next(reader)  # get headers from first row
    data = list(reader)     # exhaust iterator from second row onwards

The result is a list of headers, and a list of lists for data:

print(headers)

['play', 'weather', 'temperature']

print(data)

[['yes', 'sunny', '77'],
 ['no', 'rainy', '60'],
 ['yes', 'windy', '70']]

pandas

If you are willing to use a 3rd party library, Pandas may be a better option as it handles type conversion and indexing more conveniently:

import pandas as pd

df = pd.read_csv('file.csv')

The result is a pd.DataFrame object:

print(df)

  play weather  temperature
0  yes   sunny           77
1   no   rainy           60
2  yes   windy           70

print(type(df))

<class 'pandas.core.frame.DataFrame'>