今天聊一下数据清洗之 重复值处理

重复值处理

  • 数据清洗一般先从重复值和缺失值开始处理
  • 重复值一般采取删除法来处理
  • 但有些重复值不能删除，例如订单明细数据或交易明细数据等
import pandas as pd
import numpy as np
import os

os.getcwd()

'D:\\Jupyter\\notebook\\Python数据清洗实战\\数据清洗之数据预处理'

os.chdir('D:\\Jupyter\\notebook\\Python数据清洗实战\\数据')

df = pd.read_csv('MotorcycleData.csv', encoding='gbk', na_values='Na')

df.head(5)

<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>Condition</th>

  <th>Condition_Desc</th>

  <th>Price</th>

  <th>Location</th>

  <th>Model_Year</th>

  <th>Mileage</th>

  <th>Exterior_Color</th>

  <th>Make</th>

  <th>Warranty</th>

  <th>Model</th>

  <th>...</th>

  <th>Vehicle_Title</th>

  <th>OBO</th>

  <th>Feedback_Perc</th>

  <th>Watch_Count</th>

  <th>N_Reviews</th>

  <th>Seller_Status</th>

  <th>Vehicle_Tile</th>

  <th>Auction</th>

  <th>Buy_Now</th>

  <th>Bid_Count</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>Used</td>

  <td>mint!!! very low miles</td>

  <td>$11,412</td>

  <td>McHenry, Illinois, United States</td>

  <td>2013.0</td>

  <td>16,000</td>

  <td>Black</td>

  <td>Harley-Davidson</td>

  <td>Unspecified</td>

  <td>Touring</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>8.1</td>

  <td>NaN</td>

  <td>2427</td>

  <td>Private Seller</td>

  <td>Clear</td>

  <td>True</td>

  <td>FALSE</td>

  <td>28.0</td>

</tr>

<tr>

  <th>1</th>

  <td>Used</td>

  <td>Perfect condition</td>

  <td>$17,200</td>

  <td>Fort Recovery, Ohio, United States</td>

  <td>2016.0</td>

  <td>60</td>

  <td>Black</td>

  <td>Harley-Davidson</td>

  <td>Vehicle has an existing warranty</td>

  <td>Touring</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>100</td>

  <td>17</td>

  <td>657</td>

  <td>Private Seller</td>

  <td>Clear</td>

  <td>True</td>

  <td>TRUE</td>

  <td>0.0</td>

</tr>

<tr>

  <th>2</th>

  <td>Used</td>

  <td>NaN</td>

  <td>$3,872</td>

  <td>Chicago, Illinois, United States</td>

  <td>1970.0</td>

  <td>25,763</td>

  <td>Silver/Blue</td>

  <td>BMW</td>

  <td>Vehicle does NOT have an existing warranty</td>

  <td>R-Series</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>100</td>

  <td>NaN</td>

  <td>136</td>

  <td>NaN</td>

  <td>Clear</td>

  <td>True</td>

  <td>FALSE</td>

  <td>26.0</td>

</tr>

<tr>

  <th>3</th>

  <td>Used</td>

  <td>CLEAN TITLE   READY TO RIDE HOME</td>

  <td>$6,575</td>

  <td>Green Bay, Wisconsin, United States</td>

  <td>2009.0</td>

  <td>33,142</td>

  <td>Red</td>

  <td>Harley-Davidson</td>

  <td>NaN</td>

  <td>Touring</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>100</td>

  <td>NaN</td>

  <td>2920</td>

  <td>Dealer</td>

  <td>Clear</td>

  <td>True</td>

  <td>FALSE</td>

  <td>11.0</td>

</tr>

<tr>

  <th>4</th>

  <td>Used</td>

  <td>NaN</td>

  <td>$10,000</td>

  <td>West Bend, Wisconsin, United States</td>

  <td>2012.0</td>

  <td>17,800</td>

  <td>Blue</td>

  <td>Harley-Davidson</td>

  <td>NO WARRANTY</td>

  <td>Touring</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>100</td>

  <td>13</td>

  <td>271</td>

  <td>OWNER</td>

  <td>Clear</td>

  <td>True</td>

  <td>TRUE</td>

  <td>0.0</td>

</tr>

</tbody>

</table>

<p>5 rows × 22 columns</p>

</div>

def f(x):
    if '$' in str(x):
        x = str(x).strip('$')
        x = str(x).replace(',', '')
    else:
        x = str(x).replace(',', '')
    return float(x)

df['Price'] = df['Price'].apply(f)

df['Mileage'] = df['Mileage'].apply(f)

df.head(5)

<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>Condition</th>

  <th>Condition_Desc</th>

  <th>Price</th>

  <th>Location</th>

  <th>Model_Year</th>

  <th>Mileage</th>

  <th>Exterior_Color</th>

  <th>Make</th>

  <th>Warranty</th>

  <th>Model</th>

  <th>...</th>

  <th>Vehicle_Title</th>

  <th>OBO</th>

  <th>Feedback_Perc</th>

  <th>Watch_Count</th>

  <th>N_Reviews</th>

  <th>Seller_Status</th>

  <th>Vehicle_Tile</th>

  <th>Auction</th>

  <th>Buy_Now</th>

  <th>Bid_Count</th>

</tr>

</thead>

<tbody>

<tr>

  <th>0</th>

  <td>Used</td>

  <td>mint!!! very low miles</td>

  <td>11412.0</td>

  <td>McHenry, Illinois, United States</td>

  <td>2013.0</td>

  <td>16000.0</td>

  <td>Black</td>

  <td>Harley-Davidson</td>

  <td>Unspecified</td>

  <td>Touring</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>8.1</td>

  <td>NaN</td>

  <td>2427</td>

  <td>Private Seller</td>

  <td>Clear</td>

  <td>True</td>

  <td>FALSE</td>

  <td>28.0</td>

</tr>

<tr>

  <th>1</th>

  <td>Used</td>

  <td>Perfect condition</td>

  <td>17200.0</td>

  <td>Fort Recovery, Ohio, United States</td>

  <td>2016.0</td>

  <td>60.0</td>

  <td>Black</td>

  <td>Harley-Davidson</td>

  <td>Vehicle has an existing warranty</td>

  <td>Touring</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>100</td>

  <td>17</td>

  <td>657</td>

  <td>Private Seller</td>

  <td>Clear</td>

  <td>True</td>

  <td>TRUE</td>

  <td>0.0</td>

</tr>

<tr>

  <th>2</th>

  <td>Used</td>

  <td>NaN</td>

  <td>3872.0</td>

  <td>Chicago, Illinois, United States</td>

  <td>1970.0</td>

  <td>25763.0</td>

  <td>Silver/Blue</td>

  <td>BMW</td>

  <td>Vehicle does NOT have an existing warranty</td>

  <td>R-Series</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>100</td>

  <td>NaN</td>

  <td>136</td>

  <td>NaN</td>

  <td>Clear</td>

  <td>True</td>

  <td>FALSE</td>

  <td>26.0</td>

</tr>

<tr>

  <th>3</th>

  <td>Used</td>

  <td>CLEAN TITLE   READY TO RIDE HOME</td>

  <td>6575.0</td>

  <td>Green Bay, Wisconsin, United States</td>

  <td>2009.0</td>

  <td>33142.0</td>

  <td>Red</td>

  <td>Harley-Davidson</td>

  <td>NaN</td>

  <td>Touring</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>100</td>

  <td>NaN</td>

  <td>2920</td>

  <td>Dealer</td>

  <td>Clear</td>

  <td>True</td>

  <td>FALSE</td>

  <td>11.0</td>

</tr>

<tr>

  <th>4</th>

  <td>Used</td>

  <td>NaN</td>

  <td>10000.0</td>

  <td>West Bend, Wisconsin, United States</td>

  <td>2012.0</td>

  <td>17800.0</td>

  <td>Blue</td>

  <td>Harley-Davidson</td>

  <td>NO WARRANTY</td>

  <td>Touring</td>

  <td>...</td>

  <td>NaN</td>

  <td>FALSE</td>

  <td>100</td>

  <td>13</td>

  <td>271</td>

  <td>OWNER</td>

  <td>Clear</td>

  <td>True</td>

  <td>TRUE</td>

  <td>0.0</td>

</tr>

</tbody>

</table>

<p>5 rows × 22 columns</p>

</div>

df.info()

<class 'pandas.core.frame.DataFrame'>

RangeIndex: 7493 entries, 0 to 7492

Data columns (total 22 columns):

Condition         7493 non-null object

Condition_Desc    1656 non-null object

Price             7493 non-null float64

Location          7491 non-null object

Model_Year        7489 non-null float64

Mileage           7467 non-null float64

Exterior_Color    6778 non-null object

Make              7489 non-null object

Warranty          5108 non-null object

Model             7370 non-null object

Sub_Model         2426 non-null object

Type              6011 non-null object

Vehicle_Title     268 non-null object

OBO               7427 non-null object

Feedback_Perc     6611 non-null object

Watch_Count       3517 non-null object

N_Reviews         7487 non-null object

Seller_Status     6868 non-null object

Vehicle_Tile      7439 non-null object

Auction           7476 non-null object

Buy_Now           7256 non-null object

Bid_Count         2190 non-null float64

dtypes: float64(4), object(18)

memory usage: 1.3+ MB

any(df.duplicated())

True

# 显示重复数据
# df[df.duplicated()] 

# 统计重复数据
np.sum(df.duplicated())

1221

# 删除重复值
df.drop_duplicates(inplace=True)

df.columns

Index(['Condition', 'Condition_Desc', 'Price', 'Location', 'Model_Year',

       'Mileage', 'Exterior_Color', 'Make', 'Warranty', 'Model', 'Sub_Model',

       'Type', 'Vehicle_Title', 'OBO', 'Feedback_Perc', 'Watch_Count',

       'N_Reviews', 'Seller_Status', 'Vehicle_Tile', 'Auction', 'Buy_Now',

       'Bid_Count'],

      dtype='object')

# 根据指定变量判断重复值
df.drop_duplicates(subset=['Condition', 'Condition_Desc', 'Price', 'Location'], inplace=True)

# 重复已经被删除
np.sum(df.duplicated())

0

正文完
发表至： Python 技术教程
2023-08-04