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Data deduplication can use two methods: duplicated() and drop_duplicates().
DataFrame.duplicated(subset=None, keep='first') returns a boolean Series representing duplicate rows
Parameters:
subset: column label or label sequence, optional
Only certain columns are considered for identifying duplicates, by default all columns are used
keep: {'first', 'last', False}, default 'first'
first: Mark duplicates, True except for the first occurrence.
last: Marks duplicates, True except for the last occurrence.
Error: Mark all duplicates as True.
Related recommendations: "Python Basic Tutorial"
import numpy as np import pandas as pd from pandas import Series, DataFrame df = pd.read_csv('./demo_duplicate.csv') print(df) print(df['Seqno'].unique()) # [0. 1.] # 使用duplicated 查看重复值 # 参数 keep 可以标记重复值 {'first','last',False} print(df['Seqno'].duplicated()) ''' 0 False 1 True 2 True 3 True 4 False Name: Seqno, dtype: bool ''' # 删除 series 重复数据 print(df['Seqno'].drop_duplicates()) ''' 0 0.0 4 1.0 Name: Seqno, dtype: float64 ''' # 删除 dataframe 重复数据 print(df.drop_duplicates(['Seqno'])) # 按照 Seqno 来去重 ''' Price Seqno Symbol time 0 1623.0 0.0 APPL 1473411962 4 1649.0 1.0 APPL 1473411963 ''' # drop_dujplicates() 第二个参数 keep 包含的值 有: first、last、False print(df.drop_duplicates(['Seqno'], keep='last')) # 保存最后一个 ''' Price Seqno Symbol time 3 1623.0 0.0 APPL 1473411963 4 1649.0 1.0 APPL 1473411963 '''
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