Machine Learning | Handling missing values using SimpleImputer | Data Imputation in Pandas
Machine Learning | Handling missing values using SimpleImputer | Data Imputation in Pandas
#technologycult #simpleimputer #HandlingMissingData
Python for Machine Learning - Session # 100
Topic to be covered - Simple Imputer
a. Imputing with Mean values
b. Imputing with Median Values
c. Imputing with Mode Values
d. Imputing with Constant Values
All Playlist of this youtube channel
========================
1. Data Preprocessing in Machine Learning
• Data Preprocessing in Machine Learning| Li...
2. Confusion Matrix in Machine Learning, ML, AI
• Confusion Matrix in Machine Learning, ML, AI
3. Anaconda, Python Installation, Spyder, Jupyter Notebook, PyCharm, Graphviz
• Anaconda | Python Installation | Spyder | ...
4. Cross Validation, Sampling, train test split in Machine Learning
• Cross Validation | Sampling | train test s...
5. Drop and Delete Operations in Python Pandas
• Drop and Delete Operations in Python Pandas
6. Matrices and Vectors with python
• Matrices and Vectors with python
7. Detect Outliers in Machine Learning
• Detect Outliers in Machine Learning
8. TimeSeries preprocessing in Machine Learning
• TimeSeries preprocessing in Machine Learning
9. Handling Missing Values in Machine Learning
• Handling Missing Values in Machine Learning
10. Dummy Encoding Encoding in Machine Learning
• Label Encoding, One hot Encoding, Dummy En...
11. Data Visualisation with Python, Seaborn, Matplotlib
• Data Visualisation with Python, Matplotlib...
12. Feature Scaling in Machine Learning
• Feature Scaling in Machine Learning
13. Python 3 basics for Beginner
• Python | Python 3 Basics | Python for Begi...
14. Statistics with Python
• Statistics with Python
15. Sklearn Scikit Learn Machine Learning
• Sklearn Scikit Learn Machine Learning
16. Python Pandas Dataframe Operations
• Python Pandas Dataframe Operations
Code Starts Here
===
from sklearn.preprocessing import Imputer
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer
df = pd.read_csv('diabetes1.csv')
df2 = pd.DataFrame()
task No 1
df2['col'] = [75,88,np.nan,94,168,np.nan,543]
mean_imputer = SimpleImputer(strategy='mean')
df2.iloc[:,:] = mean_imputer.fit_transform(df2)
task No 2
df2=pd.DataFrame()
df2['col'] = [75,88,np.nan,94,168,np.nan,543]
median_imputer = SimpleImputer(strategy='median')
df2.iloc[:,:] = median_imputer.fit_transform(df2)
Task No 3
df2=pd.DataFrame()
df2['col'] = [75,88,np.nan,94,168,220,543]
median_imputer = SimpleImputer(strategy='median')
df2.iloc[:,:] = median_imputer.fit_transform(df2)
print(df2)
Task No 4
df2=pd.DataFrame()
df2['col'] = [75,88,np.nan,94,168,np.nan,543]
mode_imputer = SimpleImputer(strategy='most_frequent')
df2.iloc[:,:] = mode_imputer.fit_transform(df2)
print(df2)
Task No 5
df2=pd.DataFrame()
df2['col'] = [75,88,np.nan,94,94,np.nan,543]
mode_imputer = SimpleImputer(strategy='most_frequent')
df2.iloc[:,:] = mode_imputer.fit_transform(df2)
print(df2)
Task No 6
df2=pd.DataFrame()
df2['col'] = [75,88,np.nan,94,94,np.nan,543]
constant_imputer = SimpleImputer(strategy='constant',fill_value=100)
df2.iloc[:,:] = constant_imputer.fit_transform(df2)
print(df2)
Task No 7
Impute with Mean
df_mean = df.copy(deep=True)
mean_imputer = SimpleImputer(strategy='mean')
df_mean.iloc[:,:] = mean_imputer.fit_transform(df_mean)
print(df_mean.isnull().sum())
Task No 8
Impute wi'''th Median
df_median = df.copy(deep=True)
median_imputer = SimpleImputer(strategy='median')
df_median.iloc[:,:] = median_imputer.fit_transform(df_median)
print(df_median.isnull().sum())
Task No 9
Impute with Median
df_mode = df.copy(deep=True)
mode_imputer = SimpleImputer(strategy='most_frequent')
df_mode.iloc[:,:] = mode_imputer.fit_transform(df_mode)
print(df_mode.isnull().sum())
Task No 10
Impute with Median
df_constant = df.copy(deep=True)
constant_imputer = SimpleImputer(strategy='constant',fill_value=48)
df_constant.iloc[:,:] = constant_imputer.fit_transform(df_constant)
print(df_constant.isnull().sum())
Task No 11
Impute different columns with different strategy
df3 = df.copy(deep=True)
df3.iloc[:,1] = mean_imputer.fit_transform(df3.iloc[:,1].values.reshape(-1,1))
df3.iloc[:,2] = median_imputer.fit_transform(df3.iloc[:,2].values.reshape(-1,1))
df3.iloc[:,3] = mode_imputer.fit_transform(df3.iloc[:,3].values.reshape(-1,1))
df3.iloc[:,4] = constant_imputer.fit_transform(df3.iloc[:,4].values.reshape(-1,1))
df3.iloc[:,5] = constant_imputer.fit_transform(df3.iloc[:,5].values.reshape(-1,1))
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