Handle missing values in the Iris dataset using different methods. (Dataset: Iris built-in)¶
In [1]:
!pip install numpy
!pip install pandas
!pip install scikit-learn
Requirement already satisfied: numpy in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (2.3.2) Requirement already satisfied: pandas in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (2.3.1) Requirement already satisfied: numpy>=1.26.0 in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (from pandas) (2.3.2) Requirement already satisfied: python-dateutil>=2.8.2 in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (from pandas) (2.9.0.post0) Requirement already satisfied: pytz>=2020.1 in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (from pandas) (2025.2) Requirement already satisfied: tzdata>=2022.7 in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (from pandas) (2025.2) Requirement already satisfied: six>=1.5 in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (from python-dateutil>=2.8.2->pandas) (1.17.0) Requirement already satisfied: scikit-learn in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (1.7.1) Requirement already satisfied: numpy>=1.22.0 in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (from scikit-learn) (2.3.2) Requirement already satisfied: scipy>=1.8.0 in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (from scikit-learn) (1.16.1) Requirement already satisfied: joblib>=1.2.0 in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (from scikit-learn) (1.5.1) Requirement already satisfied: threadpoolctl>=3.1.0 in /opt/anaconda3/envs/NNM24AM045/lib/python3.13/site-packages (from scikit-learn) (3.6.0)
In [2]:
import pandas as pd
import numpy as np
from sklearn.datasets import load_iris
In [3]:
data = load_iris()
df = pd.DataFrame(data.data, columns = data.feature_names)
In [4]:
df.head()
Out[4]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
In [5]:
df.tail()
Out[5]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 145 | 6.7 | 3.0 | 5.2 | 2.3 |
| 146 | 6.3 | 2.5 | 5.0 | 1.9 |
| 147 | 6.5 | 3.0 | 5.2 | 2.0 |
| 148 | 6.2 | 3.4 | 5.4 | 2.3 |
| 149 | 5.9 | 3.0 | 5.1 | 1.8 |
In [6]:
data.feature_names
Out[6]:
['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']
In [7]:
print(data.data)
[[5.1 3.5 1.4 0.2] [4.9 3. 1.4 0.2] [4.7 3.2 1.3 0.2] [4.6 3.1 1.5 0.2] [5. 3.6 1.4 0.2] [5.4 3.9 1.7 0.4] [4.6 3.4 1.4 0.3] [5. 3.4 1.5 0.2] [4.4 2.9 1.4 0.2] [4.9 3.1 1.5 0.1] [5.4 3.7 1.5 0.2] [4.8 3.4 1.6 0.2] [4.8 3. 1.4 0.1] [4.3 3. 1.1 0.1] [5.8 4. 1.2 0.2] [5.7 4.4 1.5 0.4] [5.4 3.9 1.3 0.4] [5.1 3.5 1.4 0.3] [5.7 3.8 1.7 0.3] [5.1 3.8 1.5 0.3] [5.4 3.4 1.7 0.2] [5.1 3.7 1.5 0.4] [4.6 3.6 1. 0.2] [5.1 3.3 1.7 0.5] [4.8 3.4 1.9 0.2] [5. 3. 1.6 0.2] [5. 3.4 1.6 0.4] [5.2 3.5 1.5 0.2] [5.2 3.4 1.4 0.2] [4.7 3.2 1.6 0.2] [4.8 3.1 1.6 0.2] [5.4 3.4 1.5 0.4] [5.2 4.1 1.5 0.1] [5.5 4.2 1.4 0.2] [4.9 3.1 1.5 0.2] [5. 3.2 1.2 0.2] [5.5 3.5 1.3 0.2] [4.9 3.6 1.4 0.1] [4.4 3. 1.3 0.2] [5.1 3.4 1.5 0.2] [5. 3.5 1.3 0.3] [4.5 2.3 1.3 0.3] [4.4 3.2 1.3 0.2] [5. 3.5 1.6 0.6] [5.1 3.8 1.9 0.4] [4.8 3. 1.4 0.3] [5.1 3.8 1.6 0.2] [4.6 3.2 1.4 0.2] [5.3 3.7 1.5 0.2] [5. 3.3 1.4 0.2] [7. 3.2 4.7 1.4] [6.4 3.2 4.5 1.5] [6.9 3.1 4.9 1.5] [5.5 2.3 4. 1.3] [6.5 2.8 4.6 1.5] [5.7 2.8 4.5 1.3] [6.3 3.3 4.7 1.6] [4.9 2.4 3.3 1. ] [6.6 2.9 4.6 1.3] [5.2 2.7 3.9 1.4] [5. 2. 3.5 1. ] [5.9 3. 4.2 1.5] [6. 2.2 4. 1. ] [6.1 2.9 4.7 1.4] [5.6 2.9 3.6 1.3] [6.7 3.1 4.4 1.4] [5.6 3. 4.5 1.5] [5.8 2.7 4.1 1. ] [6.2 2.2 4.5 1.5] [5.6 2.5 3.9 1.1] [5.9 3.2 4.8 1.8] [6.1 2.8 4. 1.3] [6.3 2.5 4.9 1.5] [6.1 2.8 4.7 1.2] [6.4 2.9 4.3 1.3] [6.6 3. 4.4 1.4] [6.8 2.8 4.8 1.4] [6.7 3. 5. 1.7] [6. 2.9 4.5 1.5] [5.7 2.6 3.5 1. ] [5.5 2.4 3.8 1.1] [5.5 2.4 3.7 1. ] [5.8 2.7 3.9 1.2] [6. 2.7 5.1 1.6] [5.4 3. 4.5 1.5] [6. 3.4 4.5 1.6] [6.7 3.1 4.7 1.5] [6.3 2.3 4.4 1.3] [5.6 3. 4.1 1.3] [5.5 2.5 4. 1.3] [5.5 2.6 4.4 1.2] [6.1 3. 4.6 1.4] [5.8 2.6 4. 1.2] [5. 2.3 3.3 1. ] [5.6 2.7 4.2 1.3] [5.7 3. 4.2 1.2] [5.7 2.9 4.2 1.3] [6.2 2.9 4.3 1.3] [5.1 2.5 3. 1.1] [5.7 2.8 4.1 1.3] [6.3 3.3 6. 2.5] [5.8 2.7 5.1 1.9] [7.1 3. 5.9 2.1] [6.3 2.9 5.6 1.8] [6.5 3. 5.8 2.2] [7.6 3. 6.6 2.1] [4.9 2.5 4.5 1.7] [7.3 2.9 6.3 1.8] [6.7 2.5 5.8 1.8] [7.2 3.6 6.1 2.5] [6.5 3.2 5.1 2. ] [6.4 2.7 5.3 1.9] [6.8 3. 5.5 2.1] [5.7 2.5 5. 2. ] [5.8 2.8 5.1 2.4] [6.4 3.2 5.3 2.3] [6.5 3. 5.5 1.8] [7.7 3.8 6.7 2.2] [7.7 2.6 6.9 2.3] [6. 2.2 5. 1.5] [6.9 3.2 5.7 2.3] [5.6 2.8 4.9 2. ] [7.7 2.8 6.7 2. ] [6.3 2.7 4.9 1.8] [6.7 3.3 5.7 2.1] [7.2 3.2 6. 1.8] [6.2 2.8 4.8 1.8] [6.1 3. 4.9 1.8] [6.4 2.8 5.6 2.1] [7.2 3. 5.8 1.6] [7.4 2.8 6.1 1.9] [7.9 3.8 6.4 2. ] [6.4 2.8 5.6 2.2] [6.3 2.8 5.1 1.5] [6.1 2.6 5.6 1.4] [7.7 3. 6.1 2.3] [6.3 3.4 5.6 2.4] [6.4 3.1 5.5 1.8] [6. 3. 4.8 1.8] [6.9 3.1 5.4 2.1] [6.7 3.1 5.6 2.4] [6.9 3.1 5.1 2.3] [5.8 2.7 5.1 1.9] [6.8 3.2 5.9 2.3] [6.7 3.3 5.7 2.5] [6.7 3. 5.2 2.3] [6.3 2.5 5. 1.9] [6.5 3. 5.2 2. ] [6.2 3.4 5.4 2.3] [5.9 3. 5.1 1.8]]
In [8]:
df.iloc[::10] = np.nan
df.isnull().sum()
Out[8]:
sepal length (cm) 15 sepal width (cm) 15 petal length (cm) 15 petal width (cm) 15 dtype: int64
In [9]:
df.head(20)
Out[9]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | NaN | NaN | NaN | NaN |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
| 5 | 5.4 | 3.9 | 1.7 | 0.4 |
| 6 | 4.6 | 3.4 | 1.4 | 0.3 |
| 7 | 5.0 | 3.4 | 1.5 | 0.2 |
| 8 | 4.4 | 2.9 | 1.4 | 0.2 |
| 9 | 4.9 | 3.1 | 1.5 | 0.1 |
| 10 | NaN | NaN | NaN | NaN |
| 11 | 4.8 | 3.4 | 1.6 | 0.2 |
| 12 | 4.8 | 3.0 | 1.4 | 0.1 |
| 13 | 4.3 | 3.0 | 1.1 | 0.1 |
| 14 | 5.8 | 4.0 | 1.2 | 0.2 |
| 15 | 5.7 | 4.4 | 1.5 | 0.4 |
| 16 | 5.4 | 3.9 | 1.3 | 0.4 |
| 17 | 5.1 | 3.5 | 1.4 | 0.3 |
| 18 | 5.7 | 3.8 | 1.7 | 0.3 |
| 19 | 5.1 | 3.8 | 1.5 | 0.3 |
1. Dropna¶
In [10]:
df.dropna(inplace = True)
In [10]:
df.isnull().sum()
Out[10]:
sepal length (cm) 0 sepal width (cm) 0 petal length (cm) 0 petal width (cm) 0 dtype: int64
In [11]:
data = load_iris()
df = pd.DataFrame(data.data, columns = data.feature_names)
df.iloc[::10] = np.nan
df.isnull().sum()
Out[11]:
sepal length (cm) 15 sepal width (cm) 15 petal length (cm) 15 petal width (cm) 15 dtype: int64
2. Fillna¶
In [12]:
df.fillna(0, inplace = True)
df.isnull().sum()
Out[12]:
sepal length (cm) 0 sepal width (cm) 0 petal length (cm) 0 petal width (cm) 0 dtype: int64
In [13]:
df.head(20)
Out[13]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
| 5 | 5.4 | 3.9 | 1.7 | 0.4 |
| 6 | 4.6 | 3.4 | 1.4 | 0.3 |
| 7 | 5.0 | 3.4 | 1.5 | 0.2 |
| 8 | 4.4 | 2.9 | 1.4 | 0.2 |
| 9 | 4.9 | 3.1 | 1.5 | 0.1 |
| 10 | 0.0 | 0.0 | 0.0 | 0.0 |
| 11 | 4.8 | 3.4 | 1.6 | 0.2 |
| 12 | 4.8 | 3.0 | 1.4 | 0.1 |
| 13 | 4.3 | 3.0 | 1.1 | 0.1 |
| 14 | 5.8 | 4.0 | 1.2 | 0.2 |
| 15 | 5.7 | 4.4 | 1.5 | 0.4 |
| 16 | 5.4 | 3.9 | 1.3 | 0.4 |
| 17 | 5.1 | 3.5 | 1.4 | 0.3 |
| 18 | 5.7 | 3.8 | 1.7 | 0.3 |
| 19 | 5.1 | 3.8 | 1.5 | 0.3 |
3. Fillna using bfill¶
In [14]:
data = load_iris()
df = pd.DataFrame(data.data, columns = data.feature_names)
df.iloc[::10] = np.nan
df.isnull().sum()
Out[14]:
sepal length (cm) 15 sepal width (cm) 15 petal length (cm) 15 petal width (cm) 15 dtype: int64
In [15]:
df.fillna(method = 'bfill', inplace = True)
df.isnull().sum()
/tmp/ipykernel_12621/1599715688.py:1: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead. df.fillna(method = 'bfill', inplace = True)
Out[15]:
sepal length (cm) 0 sepal width (cm) 0 petal length (cm) 0 petal width (cm) 0 dtype: int64
In [16]:
df.head(20)
Out[16]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | 4.9 | 3.0 | 1.4 | 0.2 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
| 5 | 5.4 | 3.9 | 1.7 | 0.4 |
| 6 | 4.6 | 3.4 | 1.4 | 0.3 |
| 7 | 5.0 | 3.4 | 1.5 | 0.2 |
| 8 | 4.4 | 2.9 | 1.4 | 0.2 |
| 9 | 4.9 | 3.1 | 1.5 | 0.1 |
| 10 | 4.8 | 3.4 | 1.6 | 0.2 |
| 11 | 4.8 | 3.4 | 1.6 | 0.2 |
| 12 | 4.8 | 3.0 | 1.4 | 0.1 |
| 13 | 4.3 | 3.0 | 1.1 | 0.1 |
| 14 | 5.8 | 4.0 | 1.2 | 0.2 |
| 15 | 5.7 | 4.4 | 1.5 | 0.4 |
| 16 | 5.4 | 3.9 | 1.3 | 0.4 |
| 17 | 5.1 | 3.5 | 1.4 | 0.3 |
| 18 | 5.7 | 3.8 | 1.7 | 0.3 |
| 19 | 5.1 | 3.8 | 1.5 | 0.3 |
4. Fillna using pad¶
In [17]:
data = load_iris()
df = pd.DataFrame(data.data, columns = data.feature_names)
df.iloc[::10] = np.nan
df.isnull().sum()
Out[17]:
sepal length (cm) 15 sepal width (cm) 15 petal length (cm) 15 petal width (cm) 15 dtype: int64
In [18]:
df.fillna(method = 'pad', inplace = True)
df.isnull().sum()
/tmp/ipykernel_12621/2285065174.py:1: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead. df.fillna(method = 'pad', inplace = True)
Out[18]:
sepal length (cm) 1 sepal width (cm) 1 petal length (cm) 1 petal width (cm) 1 dtype: int64
In [20]:
df.head(20)
Out[20]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | NaN | NaN | NaN | NaN |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
| 5 | 5.4 | 3.9 | 1.7 | 0.4 |
| 6 | 4.6 | 3.4 | 1.4 | 0.3 |
| 7 | 5.0 | 3.4 | 1.5 | 0.2 |
| 8 | 4.4 | 2.9 | 1.4 | 0.2 |
| 9 | 4.9 | 3.1 | 1.5 | 0.1 |
| 10 | 4.9 | 3.1 | 1.5 | 0.1 |
| 11 | 4.8 | 3.4 | 1.6 | 0.2 |
| 12 | 4.8 | 3.0 | 1.4 | 0.1 |
| 13 | 4.3 | 3.0 | 1.1 | 0.1 |
| 14 | 5.8 | 4.0 | 1.2 | 0.2 |
| 15 | 5.7 | 4.4 | 1.5 | 0.4 |
| 16 | 5.4 | 3.9 | 1.3 | 0.4 |
| 17 | 5.1 | 3.5 | 1.4 | 0.3 |
| 18 | 5.7 | 3.8 | 1.7 | 0.3 |
| 19 | 5.1 | 3.8 | 1.5 | 0.3 |
In [21]:
df.fillna(method = 'bfill', inplace = True)
df.isnull().sum()
/tmp/ipykernel_12621/1599715688.py:1: FutureWarning: DataFrame.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead. df.fillna(method = 'bfill', inplace = True)
Out[21]:
sepal length (cm) 0 sepal width (cm) 0 petal length (cm) 0 petal width (cm) 0 dtype: int64
In [22]:
df.head(20)
Out[22]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | 4.9 | 3.0 | 1.4 | 0.2 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
| 5 | 5.4 | 3.9 | 1.7 | 0.4 |
| 6 | 4.6 | 3.4 | 1.4 | 0.3 |
| 7 | 5.0 | 3.4 | 1.5 | 0.2 |
| 8 | 4.4 | 2.9 | 1.4 | 0.2 |
| 9 | 4.9 | 3.1 | 1.5 | 0.1 |
| 10 | 4.9 | 3.1 | 1.5 | 0.1 |
| 11 | 4.8 | 3.4 | 1.6 | 0.2 |
| 12 | 4.8 | 3.0 | 1.4 | 0.1 |
| 13 | 4.3 | 3.0 | 1.1 | 0.1 |
| 14 | 5.8 | 4.0 | 1.2 | 0.2 |
| 15 | 5.7 | 4.4 | 1.5 | 0.4 |
| 16 | 5.4 | 3.9 | 1.3 | 0.4 |
| 17 | 5.1 | 3.5 | 1.4 | 0.3 |
| 18 | 5.7 | 3.8 | 1.7 | 0.3 |
| 19 | 5.1 | 3.8 | 1.5 | 0.3 |
5. Imputer method¶
In [23]:
data = load_iris()
df = pd.DataFrame(data.data, columns = data.feature_names)
df.iloc[::10] = np.nan
df.isnull().sum()
Out[23]:
sepal length (cm) 15 sepal width (cm) 15 petal length (cm) 15 petal width (cm) 15 dtype: int64
In [24]:
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy = 'mean')
df_imputed = imputer.fit_transform(df)
df_imputed = pd.DataFrame(df_imputed, columns = df.columns)
In [25]:
df_imputed.isnull().sum()
Out[25]:
sepal length (cm) 0 sepal width (cm) 0 petal length (cm) 0 petal width (cm) 0 dtype: int64
In [26]:
df_imputed.head(20)
Out[26]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | 5.837778 | 3.054815 | 3.751852 | 1.194074 |
| 1 | 4.900000 | 3.000000 | 1.400000 | 0.200000 |
| 2 | 4.700000 | 3.200000 | 1.300000 | 0.200000 |
| 3 | 4.600000 | 3.100000 | 1.500000 | 0.200000 |
| 4 | 5.000000 | 3.600000 | 1.400000 | 0.200000 |
| 5 | 5.400000 | 3.900000 | 1.700000 | 0.400000 |
| 6 | 4.600000 | 3.400000 | 1.400000 | 0.300000 |
| 7 | 5.000000 | 3.400000 | 1.500000 | 0.200000 |
| 8 | 4.400000 | 2.900000 | 1.400000 | 0.200000 |
| 9 | 4.900000 | 3.100000 | 1.500000 | 0.100000 |
| 10 | 5.837778 | 3.054815 | 3.751852 | 1.194074 |
| 11 | 4.800000 | 3.400000 | 1.600000 | 0.200000 |
| 12 | 4.800000 | 3.000000 | 1.400000 | 0.100000 |
| 13 | 4.300000 | 3.000000 | 1.100000 | 0.100000 |
| 14 | 5.800000 | 4.000000 | 1.200000 | 0.200000 |
| 15 | 5.700000 | 4.400000 | 1.500000 | 0.400000 |
| 16 | 5.400000 | 3.900000 | 1.300000 | 0.400000 |
| 17 | 5.100000 | 3.500000 | 1.400000 | 0.300000 |
| 18 | 5.700000 | 3.800000 | 1.700000 | 0.300000 |
| 19 | 5.100000 | 3.800000 | 1.500000 | 0.300000 |
In [28]:
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(strategy = 'median')
df_imputed = imputer.fit_transform(df)
df_imputed = pd.DataFrame(df_imputed, columns = df.columns)
In [29]:
df_imputed.isnull().sum()
Out[29]:
sepal length (cm) 0 sepal width (cm) 0 petal length (cm) 0 petal width (cm) 0 dtype: int64
In [30]:
df_imputed.head(20)
Out[30]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 0 | 5.8 | 3.0 | 4.3 | 1.3 |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 |
| 5 | 5.4 | 3.9 | 1.7 | 0.4 |
| 6 | 4.6 | 3.4 | 1.4 | 0.3 |
| 7 | 5.0 | 3.4 | 1.5 | 0.2 |
| 8 | 4.4 | 2.9 | 1.4 | 0.2 |
| 9 | 4.9 | 3.1 | 1.5 | 0.1 |
| 10 | 5.8 | 3.0 | 4.3 | 1.3 |
| 11 | 4.8 | 3.4 | 1.6 | 0.2 |
| 12 | 4.8 | 3.0 | 1.4 | 0.1 |
| 13 | 4.3 | 3.0 | 1.1 | 0.1 |
| 14 | 5.8 | 4.0 | 1.2 | 0.2 |
| 15 | 5.7 | 4.4 | 1.5 | 0.4 |
| 16 | 5.4 | 3.9 | 1.3 | 0.4 |
| 17 | 5.1 | 3.5 | 1.4 | 0.3 |
| 18 | 5.7 | 3.8 | 1.7 | 0.3 |
| 19 | 5.1 | 3.8 | 1.5 | 0.3 |
In [31]:
df_imputed.loc[df_imputed.index == 70]
Out[31]:
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | |
|---|---|---|---|---|
| 70 | 5.8 | 3.0 | 4.3 | 1.3 |