Completeness

Python में डेटा क्लीनिंग

Adel Nehme

VP of AI Curriculum, DataCamp

Missing data क्या है?

NA, nan, 0, . आदि के रूप में दिख सकता है

तकनीकी त्रुटि

मानवीय त्रुटि

Airquality उदाहरण

import pandas as pd
airquality = pd.read_csv('airquality.csv')
print(airquality)

            Date  Temperature  CO2
987   20/04/2004         16.8  0.0
2119  07/06/2004         18.7  0.8
2451  20/06/2004        -40.0  NaN
1984  01/06/2004         19.6  1.8
8299  19/02/2005         11.2  1.2
...      ...             ...   ...

Airquality उदाहरण

import pandas as pd
airquality = pd.read_csv('airquality.csv')
print(airquality)

            Date  Temperature  CO2
987   20/04/2004         16.8  0.0
2119  07/06/2004         18.7  0.8
2451  20/06/2004        -40.0  NaN   <--
1984  01/06/2004         19.6  1.8
8299  19/02/2005         11.2  1.2
...      ...             ...   ...

Airquality उदाहरण

# Return missing values
airquality.isna()

       Date  Temperature    CO2
987   False        False  False
2119  False        False  False
2451  False        False   True
1984  False        False  False
8299  False        False  False

Airquality उदाहरण

# Get summary of missingness
airquality.isna().sum()

Date             0
Temperature      0
CO2            366
dtype: int64

Missingno

Missing data को visualizing और समझने के लिए उपयोगी पैकेज

import missingno as msno
import matplotlib.pyplot as plt

# Visualize missingness
msno.matrix(airquality)
plt.show()

Airquality उदाहरण

# Isolate missing and complete values aside
missing = airquality[airquality['CO2'].isna()]
complete = airquality[~airquality['CO2'].isna()]

Airquality उदाहरण

# Describe complete DataFramee
complete.describe()

       Temperature          CO2
count  8991.000000  8991.000000
mean     18.317829     1.739584
std       8.832116     1.537580
min      -1.900000     0.000000
...      ...        ...
max      44.600000    11.900000

# Describe missing DataFramee
missing.describe()

       Temperature  CO2
count   366.000000  0.0
mean    -39.655738  NaN
std       5.988716  NaN
min     -49.000000  NaN
...      ...        ...
max     -30.000000  NaN

Airquality उदाहरण

# Describe complete DataFramee
complete.describe()

       Temperature          CO2
count  8991.000000  8991.000000
mean     18.317829     1.739584
std       8.832116     1.537580
min      -1.900000     0.000000
...      ...        ...
max      44.600000    11.900000

# Describe missing DataFramee
missing.describe()

       Temperature  CO2
count   366.000000  0.0
mean    -39.655738  NaN   <--
std       5.988716  NaN
min     -49.000000  NaN   <--
...      ...        ...
max     -30.000000  NaN   <--

sorted_airquality = airquality.sort_values(by = 'Temperature')
msno.matrix(sorted_airquality)
plt.show()

sorted_airquality = airquality.sort_values(by = 'Temperature')
msno.matrix(sorted_airquality)
plt.show()

Missingness के प्रकार

Missing data से कैसे निपटें?

सरल तरीके:

Missing data हटाएँ
सांख्यिकीय मानों से इम्प्यूट करें (mean, median, mode...)

और जटिल तरीके:

एल्गोरिदमिक तरीके से इम्प्यूट करें
Machine Learning मॉडल से इम्प्यूट करें

Missing data से निपटना

airquality.head()

         Date  Temperature  CO2
0  05/03/2005          8.5  2.5
1  23/08/2004         21.8  0.0
2  18/02/2005          6.3  1.0
3  08/02/2005        -31.0  NaN
4  13/03/2005         19.9  0.1

Missing values हटाना

# Drop missing values
airquality_dropped = airquality.dropna(subset = ['CO2'])
airquality_dropped.head()

         Date  Temperature  CO2
0  05/03/2005          8.5  2.5
1  23/08/2004         21.8  0.0
2  18/02/2005          6.3  1.0
4  13/03/2005         19.9  0.1
5  02/04/2005         17.0  0.8

सांख्यिकीय मानों से बदलना

co2_mean = airquality['CO2'].mean()
airquality_imputed = airquality.fillna({'CO2': co2_mean})
airquality_imputed.head()

          Date  Temperature       CO2
0  05/03/2005          8.5  2.500000
1  23/08/2004         21.8  0.000000
2  18/02/2005          6.3  1.000000
3  08/02/2005        -31.0  1.739584
4  13/03/2005         19.9  0.100000

अभ्यास करते हैं!

Python में डेटा क्लीनिंग