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()

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

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'>

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

The csv.DictReader function expects a file object as its first argument, not a string. So, it always is used with normal file io.

for example,

with open('input.csv', 'r') as f:
    reader = csv.DictReader(f, delimiter=' ', fieldnames = ['Device', 'tps', 'kB_read/s' ,'kB_wrtn/s', 'kB_dscd/s', 'kB_read', 'kB_wrtn', 'kB_dscd'])

And, you can iterate the reader (i.e., iterable of dicts) row by row.

for row in reader:
    print(row)

The row variable contains dictionary-like object that you can easily access by key, for example, row['Device'].

You could use a library like pandas, it will infer the types for you (it's a bit of an overkill but it does the job).

import pandas
data = pandas.read_csv(r'..\data\data.csv')
# if you just want to retrieve the first column as a list of int do
list(data.Col1)
>>> [1, 90]

# to convert the whole CSV file to a list of dict use
data.transpose().to_dict().values()
>>> [{' Col2': 2, ' Col3': 3, 'Col1': 1}, {' Col2': 2, ' Col3': 3, 'Col1': 90}]

Alternatively here is an implementation of a typed DictReader:

from csv import DictReader
from itertools import imap, izip

class TypedDictReader(DictReader):
  def __init__(self, f, fieldnames=None, restkey=None, restval=None, \
      dialect="excel", fieldtypes=None, *args, **kwds):

    DictReader.__init__(self, f, fieldnames, restkey, restval, dialect, *args, **kwds)
    self._fieldtypes = fieldtypes

  def next(self):
    d = DictReader.next(self)
    if len(self._fieldtypes) >= len(d) :
      # extract the values in the same order as the csv header
      ivalues = imap(d.get, self._fieldnames) 
      # apply type conversions
      iconverted = (x(y) for (x,y) in izip(self._fieldtypes, ivalues)) 
      # pass the field names and the converted values to the dict constructor
      d = dict(izip(self._fieldnames, iconverted)) 

    return d

and here is how to use it:

reader = TypedDictReader(open('..\data\data.csv'), dialect='excel', \
  fieldtypes=[int, int, int])
list(reader)
>>> [{' Col2': 2, ' Col3': 3, 'Col1': 1}, {' Col2': 2, ' Col3': 3, 'Col1': 90}]