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))
Auf dieser Seite können Sie das Online-Video Machine Learning | Handling missing values using SimpleImputer | Data Imputation in Pandas mit der Dauer stunde minuten sekunde in guter Qualität ansehen, das der Benutzer technologyCult 01 Januar 1970 hochgeladen hat, den Link mit Freunden und Bekannten teilen, dieses Video wurde auf Youtube bereits 5,759 Mal angesehen und es wurde von 88 den Zuschauern gefallen. Viel Spaß beim Betrachtenden Zuschauern gefallen!
