How does Scrapy clean and process crawler data?

Crawler data processing is a crucial step in data collection applications. Scrapy is a popular Python crawler framework that can help us extract the required information from web pages quickly and efficiently. However, a problem we often face is that the data are of poor quality, with various noises and errors, which makes them difficult to use for subsequent analysis and decision-making. Therefore, crawler data must be cleaned and preprocessed before performing tasks such as data mining and machine learning. This article will introduce how Scrapy cleans and processes crawler data.

1. Data Cleaning

Data cleaning refers to removing errors, incomplete or useless data during the data processing stage to make the data more standardized and reliable. The following are some common data cleaning techniques:

1) Remove duplicate values: Scrapy can detect and delete duplicate data, use a simple command as follows:

from scrapy.utils import dupefilter
from scrapy.dupefilters import RFPDupeFilter

dupefilter.RFPDupeFilter.from_settings(settings)

2) Fill in missing values: Scrapy can Use fillna() method to fill missing values. For example, replace missing values ​​with the mean or median of the data:

df.fillna(df.mean())
df.fillna(df.median())

3) Outlier detection and exclusion: Scrapy can use the Z-score method to detect and exclude outliers. Z-score is a standardization method that measures the dispersion of observations by calculating the difference between each observation and its sample mean. Observations with a Z-score exceeding 3 can be considered outliers and should be excluded.

df[df.Zscore < 3]

2. Data conversion

Data conversion refers to the conversion of data from one form to another to meet the requirements of a specific task. Here are some common data conversion techniques:

1) Normalization: Scrapy can use the Min-Max method to convert data values ​​into a range between 0 and 1. This transformation can be used to compare and unify data values ​​in different ranges.

df_norm = (df - df.min()) / (df.max() - df.min())

2) Standardization: Scrapy can use the Z-score method to transform data into a distribution with a mean of 0 and a standard deviation of 1. This transformation can be used to unify data values ​​of different scales and units to the same scale.

df_stand = (df - df.mean()) / df.std()

3) Discretization: Scrapy can use the Pandas.cut() method to discretize continuous data values ​​into several interval values. This transformation can be used to convert continuous numeric variables into categorical variables.

df['bins'] = pd.cut(df['formattime'], bins=[0,4,8,12,16,20,24], labels=['0-4', '4-8', '8-12', '12-16', '16-20', '20-24'])

3. Data integration

Data integration refers to combining data sets from different sources and formats into one data set for analysis and application. The following are some common data integration techniques:

1) Merge: Scrapy can use the Pandas.merge() method to merge two data sets with the same or different columns into one data set. This merging can be used to combine data sets from different time periods and locations into one large data set.

df_merge = pd.merge(df1, df2, on='id')

2) Connection: Scrapy can use the Pandas.concat() method to join two datasets with the same or different indexes into one dataset. This join can be used to join datasets from the same time period and location together.

df_concat=pd.concat([df1,df2])

3) Stacking: Scrapy can use the Pandas.stack() method to convert a set of column values ​​into a set of row values. This transformation can be used to convert a dataset from wide format to long format.

df_stacked = df.stack()

In summary, Scrapy is a powerful crawler framework that has a high degree of freedom and flexibility in data cleaning and processing. Various methods using Scrapy and Pandas libraries can help us clean and process raw crawler data effectively, thereby improving data quality and reliability.

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