---
# System prepended metadata

title: Virgil - Data Cleaning - S11 Determine Important Features
tags: [' LearnWorld', ' DataCleaning', Virgil]

---

---
title: Virgil - Data Cleaning - S11 Determine Important Features
tags: Virgil, LearnWorld, DataCleaning
---

<a target="_blank" href="https://colab.research.google.com/drive/1GEtYEtHp7wIywIdCv1ffJq704hRoMDf_"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>

```
import pandas as pd
```

## ❊ Overview


```
df = pd.read_csv('https://raw.githubusercontent.com/dhminh1024/practice_datasets/master/titanic.csv')
```


```
df.sample(10)
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>PassengerId</th>
      <th>Survived</th>
      <th>Pclass</th>
      <th>Name</th>
      <th>Sex</th>
      <th>Age</th>
      <th>SibSp</th>
      <th>Parch</th>
      <th>Ticket</th>
      <th>Fare</th>
      <th>Cabin</th>
      <th>Embarked</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>619</th>
      <td>620</td>
      <td>0</td>
      <td>2</td>
      <td>Gavey, Mr. Lawrence</td>
      <td>male</td>
      <td>26.00</td>
      <td>0</td>
      <td>0</td>
      <td>31028</td>
      <td>10.5000</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>514</th>
      <td>515</td>
      <td>0</td>
      <td>3</td>
      <td>Coleff, Mr. Satio</td>
      <td>male</td>
      <td>24.00</td>
      <td>0</td>
      <td>0</td>
      <td>349209</td>
      <td>7.4958</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>496</th>
      <td>497</td>
      <td>1</td>
      <td>1</td>
      <td>Eustis, Miss. Elizabeth Mussey</td>
      <td>female</td>
      <td>54.00</td>
      <td>1</td>
      <td>0</td>
      <td>36947</td>
      <td>78.2667</td>
      <td>D20</td>
      <td>C</td>
    </tr>
    <tr>
      <th>490</th>
      <td>491</td>
      <td>0</td>
      <td>3</td>
      <td>Hagland, Mr. Konrad Mathias Reiersen</td>
      <td>male</td>
      <td>NaN</td>
      <td>1</td>
      <td>0</td>
      <td>65304</td>
      <td>19.9667</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>198</th>
      <td>199</td>
      <td>1</td>
      <td>3</td>
      <td>Madigan, Miss. Margaret "Maggie"</td>
      <td>female</td>
      <td>NaN</td>
      <td>0</td>
      <td>0</td>
      <td>370370</td>
      <td>7.7500</td>
      <td>NaN</td>
      <td>Q</td>
    </tr>
    <tr>
      <th>50</th>
      <td>51</td>
      <td>0</td>
      <td>3</td>
      <td>Panula, Master. Juha Niilo</td>
      <td>male</td>
      <td>7.00</td>
      <td>4</td>
      <td>1</td>
      <td>3101295</td>
      <td>39.6875</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>388</th>
      <td>389</td>
      <td>0</td>
      <td>3</td>
      <td>Sadlier, Mr. Matthew</td>
      <td>male</td>
      <td>NaN</td>
      <td>0</td>
      <td>0</td>
      <td>367655</td>
      <td>7.7292</td>
      <td>NaN</td>
      <td>Q</td>
    </tr>
    <tr>
      <th>524</th>
      <td>525</td>
      <td>0</td>
      <td>3</td>
      <td>Kassem, Mr. Fared</td>
      <td>male</td>
      <td>NaN</td>
      <td>0</td>
      <td>0</td>
      <td>2700</td>
      <td>7.2292</td>
      <td>NaN</td>
      <td>C</td>
    </tr>
    <tr>
      <th>644</th>
      <td>645</td>
      <td>1</td>
      <td>3</td>
      <td>Baclini, Miss. Eugenie</td>
      <td>female</td>
      <td>0.75</td>
      <td>2</td>
      <td>1</td>
      <td>2666</td>
      <td>19.2583</td>
      <td>NaN</td>
      <td>C</td>
    </tr>
    <tr>
      <th>703</th>
      <td>704</td>
      <td>0</td>
      <td>3</td>
      <td>Gallagher, Mr. Martin</td>
      <td>male</td>
      <td>25.00</td>
      <td>0</td>
      <td>0</td>
      <td>36864</td>
      <td>7.7417</td>
      <td>NaN</td>
      <td>Q</td>
    </tr>
  </tbody>
</table>
</div>




```
# Show dataset information
df.info()
```

    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 891 entries, 0 to 890
    Data columns (total 12 columns):
     #   Column       Non-Null Count  Dtype  
    ---  ------       --------------  -----  
     0   PassengerId  891 non-null    int64  
     1   Survived     891 non-null    int64  
     2   Pclass       891 non-null    int64  
     3   Name         891 non-null    object 
     4   Sex          891 non-null    object 
     5   Age          714 non-null    float64
     6   SibSp        891 non-null    int64  
     7   Parch        891 non-null    int64  
     8   Ticket       891 non-null    object 
     9   Fare         891 non-null    float64
     10  Cabin        204 non-null    object 
     11  Embarked     889 non-null    object 
    dtypes: float64(2), int64(5), object(5)
    memory usage: 83.7+ KB


## 1. Remove unwanted data

Not all the provided data is neccessary for the analysis, or in other words, able to answer the defined questions. It can be either that we only need certain number of columns certain number of rows. For example, sale data from 2009 to 2012 only, not the entire time. 


One of the early stage of data cleaning is to filter out those irrelavant data.  


```
# You might want to have a copy of the original data.
df2 = df.copy()
```


```
df2.head()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>PassengerId</th>
      <th>Survived</th>
      <th>Pclass</th>
      <th>Name</th>
      <th>Sex</th>
      <th>Age</th>
      <th>SibSp</th>
      <th>Parch</th>
      <th>Ticket</th>
      <th>Fare</th>
      <th>Cabin</th>
      <th>Embarked</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>0</td>
      <td>3</td>
      <td>Braund, Mr. Owen Harris</td>
      <td>male</td>
      <td>22.0</td>
      <td>1</td>
      <td>0</td>
      <td>A/5 21171</td>
      <td>7.2500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
      <td>female</td>
      <td>38.0</td>
      <td>1</td>
      <td>0</td>
      <td>PC 17599</td>
      <td>71.2833</td>
      <td>C85</td>
      <td>C</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>1</td>
      <td>3</td>
      <td>Heikkinen, Miss. Laina</td>
      <td>female</td>
      <td>26.0</td>
      <td>0</td>
      <td>0</td>
      <td>STON/O2. 3101282</td>
      <td>7.9250</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>1</td>
      <td>1</td>
      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
      <td>female</td>
      <td>35.0</td>
      <td>1</td>
      <td>0</td>
      <td>113803</td>
      <td>53.1000</td>
      <td>C123</td>
      <td>S</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>0</td>
      <td>3</td>
      <td>Allen, Mr. William Henry</td>
      <td>male</td>
      <td>35.0</td>
      <td>0</td>
      <td>0</td>
      <td>373450</td>
      <td>8.0500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
  </tbody>
</table>
</div>



The implementation on code is easy with `drop`. 


```
df2.drop(columns=['Pclass'])
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>PassengerId</th>
      <th>Survived</th>
      <th>Name</th>
      <th>Sex</th>
      <th>Age</th>
      <th>SibSp</th>
      <th>Parch</th>
      <th>Ticket</th>
      <th>Fare</th>
      <th>Cabin</th>
      <th>Embarked</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>0</td>
      <td>Braund, Mr. Owen Harris</td>
      <td>male</td>
      <td>22.0</td>
      <td>1</td>
      <td>0</td>
      <td>A/5 21171</td>
      <td>7.2500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>1</td>
      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
      <td>female</td>
      <td>38.0</td>
      <td>1</td>
      <td>0</td>
      <td>PC 17599</td>
      <td>71.2833</td>
      <td>C85</td>
      <td>C</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>1</td>
      <td>Heikkinen, Miss. Laina</td>
      <td>female</td>
      <td>26.0</td>
      <td>0</td>
      <td>0</td>
      <td>STON/O2. 3101282</td>
      <td>7.9250</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>1</td>
      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
      <td>female</td>
      <td>35.0</td>
      <td>1</td>
      <td>0</td>
      <td>113803</td>
      <td>53.1000</td>
      <td>C123</td>
      <td>S</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>0</td>
      <td>Allen, Mr. William Henry</td>
      <td>male</td>
      <td>35.0</td>
      <td>0</td>
      <td>0</td>
      <td>373450</td>
      <td>8.0500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>886</th>
      <td>887</td>
      <td>0</td>
      <td>Montvila, Rev. Juozas</td>
      <td>male</td>
      <td>27.0</td>
      <td>0</td>
      <td>0</td>
      <td>211536</td>
      <td>13.0000</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>887</th>
      <td>888</td>
      <td>1</td>
      <td>Graham, Miss. Margaret Edith</td>
      <td>female</td>
      <td>19.0</td>
      <td>0</td>
      <td>0</td>
      <td>112053</td>
      <td>30.0000</td>
      <td>B42</td>
      <td>S</td>
    </tr>
    <tr>
      <th>888</th>
      <td>889</td>
      <td>0</td>
      <td>Johnston, Miss. Catherine Helen "Carrie"</td>
      <td>female</td>
      <td>NaN</td>
      <td>1</td>
      <td>2</td>
      <td>W./C. 6607</td>
      <td>23.4500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>889</th>
      <td>890</td>
      <td>1</td>
      <td>Behr, Mr. Karl Howell</td>
      <td>male</td>
      <td>26.0</td>
      <td>0</td>
      <td>0</td>
      <td>111369</td>
      <td>30.0000</td>
      <td>C148</td>
      <td>C</td>
    </tr>
    <tr>
      <th>890</th>
      <td>891</td>
      <td>0</td>
      <td>Dooley, Mr. Patrick</td>
      <td>male</td>
      <td>32.0</td>
      <td>0</td>
      <td>0</td>
      <td>370376</td>
      <td>7.7500</td>
      <td>NaN</td>
      <td>Q</td>
    </tr>
  </tbody>
</table>
<p>891 rows × 11 columns</p>
</div>




```
df2.head()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>PassengerId</th>
      <th>Survived</th>
      <th>Pclass</th>
      <th>Name</th>
      <th>Sex</th>
      <th>Age</th>
      <th>SibSp</th>
      <th>Parch</th>
      <th>Ticket</th>
      <th>Fare</th>
      <th>Cabin</th>
      <th>Embarked</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>0</td>
      <td>3</td>
      <td>Braund, Mr. Owen Harris</td>
      <td>male</td>
      <td>22.0</td>
      <td>1</td>
      <td>0</td>
      <td>A/5 21171</td>
      <td>7.2500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
      <td>female</td>
      <td>38.0</td>
      <td>1</td>
      <td>0</td>
      <td>PC 17599</td>
      <td>71.2833</td>
      <td>C85</td>
      <td>C</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>1</td>
      <td>3</td>
      <td>Heikkinen, Miss. Laina</td>
      <td>female</td>
      <td>26.0</td>
      <td>0</td>
      <td>0</td>
      <td>STON/O2. 3101282</td>
      <td>7.9250</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>1</td>
      <td>1</td>
      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
      <td>female</td>
      <td>35.0</td>
      <td>1</td>
      <td>0</td>
      <td>113803</td>
      <td>53.1000</td>
      <td>C123</td>
      <td>S</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>0</td>
      <td>3</td>
      <td>Allen, Mr. William Henry</td>
      <td>male</td>
      <td>35.0</td>
      <td>0</td>
      <td>0</td>
      <td>373450</td>
      <td>8.0500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
  </tbody>
</table>
</div>




```
# Drop columns 
df2.drop(columns=['Cabin', 'Embarked'], inplace=True) # Cach 1
df2 = df2.drop(columns=['Cabin', 'Embarked']) #Cach 2

# Drop rows
df2.drop(index=[0, 2, 3], inplace=True)
```


```
df2
```

Change the name of specific column ▸ {old name: new name}


```
df2 = df2.rename(columns={'Name':'PassengerName', 'SibSp':'NumberSibling'})
df2
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>PassengerId</th>
      <th>Survived</th>
      <th>Pclass</th>
      <th>PassengerName</th>
      <th>Sex</th>
      <th>Age</th>
      <th>NumberSibling</th>
      <th>Parch</th>
      <th>Ticket</th>
      <th>Fare</th>
      <th>Cabin</th>
      <th>Embarked</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>0</td>
      <td>3</td>
      <td>Braund, Mr. Owen Harris</td>
      <td>male</td>
      <td>22.0</td>
      <td>1</td>
      <td>0</td>
      <td>A/5 21171</td>
      <td>7.2500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
      <td>female</td>
      <td>38.0</td>
      <td>1</td>
      <td>0</td>
      <td>PC 17599</td>
      <td>71.2833</td>
      <td>C85</td>
      <td>C</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>1</td>
      <td>3</td>
      <td>Heikkinen, Miss. Laina</td>
      <td>female</td>
      <td>26.0</td>
      <td>0</td>
      <td>0</td>
      <td>STON/O2. 3101282</td>
      <td>7.9250</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>1</td>
      <td>1</td>
      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
      <td>female</td>
      <td>35.0</td>
      <td>1</td>
      <td>0</td>
      <td>113803</td>
      <td>53.1000</td>
      <td>C123</td>
      <td>S</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>0</td>
      <td>3</td>
      <td>Allen, Mr. William Henry</td>
      <td>male</td>
      <td>35.0</td>
      <td>0</td>
      <td>0</td>
      <td>373450</td>
      <td>8.0500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>...</th>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
      <td>...</td>
    </tr>
    <tr>
      <th>886</th>
      <td>887</td>
      <td>0</td>
      <td>2</td>
      <td>Montvila, Rev. Juozas</td>
      <td>male</td>
      <td>27.0</td>
      <td>0</td>
      <td>0</td>
      <td>211536</td>
      <td>13.0000</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>887</th>
      <td>888</td>
      <td>1</td>
      <td>1</td>
      <td>Graham, Miss. Margaret Edith</td>
      <td>female</td>
      <td>19.0</td>
      <td>0</td>
      <td>0</td>
      <td>112053</td>
      <td>30.0000</td>
      <td>B42</td>
      <td>S</td>
    </tr>
    <tr>
      <th>888</th>
      <td>889</td>
      <td>0</td>
      <td>3</td>
      <td>Johnston, Miss. Catherine Helen "Carrie"</td>
      <td>female</td>
      <td>NaN</td>
      <td>1</td>
      <td>2</td>
      <td>W./C. 6607</td>
      <td>23.4500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>889</th>
      <td>890</td>
      <td>1</td>
      <td>1</td>
      <td>Behr, Mr. Karl Howell</td>
      <td>male</td>
      <td>26.0</td>
      <td>0</td>
      <td>0</td>
      <td>111369</td>
      <td>30.0000</td>
      <td>C148</td>
      <td>C</td>
    </tr>
    <tr>
      <th>890</th>
      <td>891</td>
      <td>0</td>
      <td>3</td>
      <td>Dooley, Mr. Patrick</td>
      <td>male</td>
      <td>32.0</td>
      <td>0</td>
      <td>0</td>
      <td>370376</td>
      <td>7.7500</td>
      <td>NaN</td>
      <td>Q</td>
    </tr>
  </tbody>
</table>
<p>891 rows × 12 columns</p>
</div>



Change all the columns' names. Names must be in the same order with the columns. 


```
df2.columns = ['A', 'B', 'C', 'D', 'E', 'F', '7', '8', '9', '10', '11', '12']
```


```
df2.head()
```




<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>A</th>
      <th>B</th>
      <th>C</th>
      <th>D</th>
      <th>E</th>
      <th>F</th>
      <th>7</th>
      <th>8</th>
      <th>9</th>
      <th>10</th>
      <th>11</th>
      <th>12</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>0</td>
      <td>3</td>
      <td>Braund, Mr. Owen Harris</td>
      <td>male</td>
      <td>22.0</td>
      <td>1</td>
      <td>0</td>
      <td>A/5 21171</td>
      <td>7.2500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>1</th>
      <td>2</td>
      <td>1</td>
      <td>1</td>
      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>
      <td>female</td>
      <td>38.0</td>
      <td>1</td>
      <td>0</td>
      <td>PC 17599</td>
      <td>71.2833</td>
      <td>C85</td>
      <td>C</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3</td>
      <td>1</td>
      <td>3</td>
      <td>Heikkinen, Miss. Laina</td>
      <td>female</td>
      <td>26.0</td>
      <td>0</td>
      <td>0</td>
      <td>STON/O2. 3101282</td>
      <td>7.9250</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4</td>
      <td>1</td>
      <td>1</td>
      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>
      <td>female</td>
      <td>35.0</td>
      <td>1</td>
      <td>0</td>
      <td>113803</td>
      <td>53.1000</td>
      <td>C123</td>
      <td>S</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5</td>
      <td>0</td>
      <td>3</td>
      <td>Allen, Mr. William Henry</td>
      <td>male</td>
      <td>35.0</td>
      <td>0</td>
      <td>0</td>
      <td>373450</td>
      <td>8.0500</td>
      <td>NaN</td>
      <td>S</td>
    </tr>
  </tbody>
</table>
</div>



## 2. Check and change datatype


```
# Check datatype
df.info()
```

    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 891 entries, 0 to 890
    Data columns (total 12 columns):
     #   Column       Non-Null Count  Dtype  
    ---  ------       --------------  -----  
     0   PassengerId  891 non-null    int64  
     1   Survived     891 non-null    int64  
     2   Pclass       891 non-null    int64  
     3   Name         891 non-null    object 
     4   Sex          891 non-null    object 
     5   Age          714 non-null    float64
     6   SibSp        891 non-null    int64  
     7   Parch        891 non-null    int64  
     8   Ticket       891 non-null    object 
     9   Fare         891 non-null    float64
     10  Cabin        204 non-null    object 
     11  Embarked     889 non-null    object 
    dtypes: float64(2), int64(5), object(5)
    memory usage: 83.7+ KB



```
# Using astype to change the datatype
df['Age'] = df['Age'].astype('int')
```


```
df.info()
```

    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 891 entries, 0 to 890
    Data columns (total 12 columns):
     #   Column       Non-Null Count  Dtype  
    ---  ------       --------------  -----  
     0   PassengerId  891 non-null    int64  
     1   Survived     891 non-null    int64  
     2   Pclass       891 non-null    int64  
     3   Name         891 non-null    object 
     4   Sex          891 non-null    object 
     5   Age          714 non-null    float64
     6   SibSp        891 non-null    int64  
     7   Parch        891 non-null    int64  
     8   Ticket       891 non-null    object 
     9   Fare         891 non-null    float64
     10  Cabin        204 non-null    object 
     11  Embarked     889 non-null    object 
    dtypes: float64(2), int64(5), object(5)
    memory usage: 83.7+ KB

