Data Quality Checks

Key Findings:

  • Currency fields are all stored as strings ($XXX.XX) and need a conversion to float values

    • match_amount

    • crowdfund_amount_contributions_usd

    • total

  • There are null values in the total and crowdfund_amount_contributions_usd fields that should be labeled 0

  • The region column is not complete, with 44% of records (2600/5900) having a value of none or undefined

    • There is not a meaningful difference between these labels, so I am combining them and coding nulls as none

    • Since there are so many missing values, this likely won’t be useful for analysis

from decimal import Decimal
from re import sub
import pandas as pd
from pandas_profiling import ProfileReport
# some data cleaning functions
def parseMoney(money):
    money = Decimal(sub(r'[^\d.]', '', money))
    return money
df = pd.read_csv('../data/grants_data_raw.csv')
# inspect it
df.head()
round_number round_start_date round_end_date grant_title grant_id region category url match_amount num_contributions num_unique_contributors crowdfund_amount_contributions_usd total
0 12 2021-12-01 2021-12-16 Coin Center is educating policy makers about p... 1668 north_america Community https://gitcoin.co/grants/1668/coin-center-is-... $340,000.00 6914 5727 $103,838.93 $443,838.93
1 12 2021-12-01 2021-12-16 Electronic Frontier Foundation 3974 north_america Community https://gitcoin.co/grants/3974/electronic-fron... $228,273.84 3648 3331 $58,715.04 $286,988.88
2 12 2021-12-01 2021-12-16 The Tor Project 2805 undefined Infra Tech https://gitcoin.co/grants/2805/the-tor-project $188,294.80 3497 3069 $95,279.64 $283,574.44
3 12 2021-12-01 2021-12-16 Longevity Prize (by VitaDAO) 4083 europe Grants Round 12 https://gitcoin.co/grants/4083/longevity-prize... $176,195.45 920 857 $7,815.29 $184,010.75
4 12 2021-12-01 2021-12-16 Rotki - The portfolio tracker and accounting t... 149 europe dApp Tech https://gitcoin.co/grants/149/rotki-the-portfo... $129,131.34 4684 4311 $25,373.62 $154,504.96
df.dtypes
round_number                           int64
round_start_date                      object
round_end_date                        object
grant_title                           object
grant_id                               int64
region                                object
category                              object
url                                   object
match_amount                          object
num_contributions                      int64
num_unique_contributors                int64
crowdfund_amount_contributions_usd    object
total                                 object
dtype: object
# parse currency fields into float columns
df['match_amount'] = pd.to_numeric(df['match_amount'].str.replace('[^.0-9]', ''))
df['crowdfund_amount_contributions_usd'] = pd.to_numeric(df['crowdfund_amount_contributions_usd'].str.replace('[^.0-9]', '')).fillna(0)
df['total'] = pd.to_numeric(df['total'].str.replace('[^.0-9]', '')).fillna(0)
/var/folders/x4/w5cvp_v94hj1jb6h056qs6r80000gp/T/ipykernel_34847/3482547301.py:2: FutureWarning: The default value of regex will change from True to False in a future version.
  df['match_amount'] = pd.to_numeric(df['match_amount'].str.replace('[^.0-9]', ''))
/var/folders/x4/w5cvp_v94hj1jb6h056qs6r80000gp/T/ipykernel_34847/3482547301.py:3: FutureWarning: The default value of regex will change from True to False in a future version.
  df['crowdfund_amount_contributions_usd'] = pd.to_numeric(df['crowdfund_amount_contributions_usd'].str.replace('[^.0-9]', '')).fillna(0)
/var/folders/x4/w5cvp_v94hj1jb6h056qs6r80000gp/T/ipykernel_34847/3482547301.py:4: FutureWarning: The default value of regex will change from True to False in a future version.
  df['total'] = pd.to_numeric(df['total'].str.replace('[^.0-9]', '')).fillna(0)
# recode the region
df['region'] = df['region'].replace('undefined', 'none').fillna('none')
df.describe()
round_number grant_id match_amount num_contributions num_unique_contributors crowdfund_amount_contributions_usd total
count 5906.000000 5906.000000 5906.000000 5906.000000 5906.000000 5906.000000 5906.000000
mean 9.399763 1449.218591 1146.815586 228.686590 186.696072 1584.362587 2731.178214
std 2.629641 1104.701308 7916.396923 934.435539 736.134848 10681.589561 15436.351606
min 1.000000 12.000000 0.000000 1.000000 0.000000 0.000000 0.000000
25% 8.000000 470.250000 0.000000 2.000000 2.000000 2.452500 3.000000
50% 10.000000 1240.500000 0.660000 9.000000 8.000000 42.395000 50.205000
75% 12.000000 2202.000000 59.182500 57.000000 47.000000 504.605000 675.857500
max 12.000000 4367.000000 340000.000000 22240.000000 12317.000000 652919.460000 674332.580000
# profile the data
profile = ProfileReport(df, title="Grants Data Profile")
profile