# Check duplicate rowsdf.duplicated()# Check the number of duplicate rowsdf.duplicated().sum()drop_duplates()可以使用这个方法删除重复的行。# Drop duplicate rows (but only keep the first row)df = df.drop_duplicates(keep='first') #keep='first' / keep='last' / keep=False# Note: in...
可能是合并的结果df.columns出发地：https://pandas.pydata.org/pandas-docs/stable/reference/api/panda...
df2.columns = pd.MultiIndex.from_product([['level1'],['level2'],df2.columns ]) df=pd.concat([df,df2],axis=1) -下拉索引不起作用 发布于 6 月前 您可以尝试: mask=(df.T.duplicated() | (df.columns.get_level_values(2).isin(['A','D']))) Finally: df=df.loc[:, mask] #OR ...
我尝试了各种.duplicated方法,但迄今为止没有一种有效。如果只获取列x中某个值的第一个实例,则应排除应包含的行(例如第7行)。 任何帮助都将不胜感激。 set并创建自定义函数:
# Check duplicate rows df.duplicated() # Check the number of duplicate rows df.duplicated().sum() drop_duplates()可以使用这个方法删除重复的行。 # Drop duplicate rows (but only keep the first row) df = df.drop_duplicates(keep='first') #keep='first' / keep='last' / keep=False # No...
print(df.duplicated()) Try it Yourself » Removing DuplicatesTo remove duplicates, use the drop_duplicates() method.Example Remove all duplicates: df.drop_duplicates(inplace = True) Try it Yourself » Remember: The (inplace = True) will make sure that the method does NOT return a ne...
'duplicate_rows': df.duplicated().sum(), 'data_types': df.dtypes.value_counts().to_dict(), 'unique_values': {col: df[col].nunique() for col in df.columns} } return pd.DataFrame(report.items(), columns=['Metric', 'Value']) 数据质量改进:class DataQualityImprover: def __init__...
# Example 3: Remove duplicate columns pandas DataFrame df2 = df.loc[:,~df.columns.duplicated()] # Example 4: Remove repeated columns in a DataFrame df2 = df.loc[:,~df.T.duplicated(keep='first')] # Example 5: Keep last duplicate columns ...
How to drop duplicated columns data based on column name in pandas Question: Assume I have a table like below A B C B 0 0 1 2 3 1 4 5 6 7 I'm looking to remove column B, but usingdrop_duplicatesonly seems to work for duplicate data rather than column headers. If anyone has a...
In [13]: df2 Out[13]: A a 0 a 1 b 2 In [14]: df2.index.is_unique Out[14]: False In [15]: df2.columns.is_unique Out[15]: True 注意 检查索引是否唯一对于大型数据集来说是比较昂贵的。pandas 会缓存此结果,因此在相同的索引上重新检查非常快。 Index.duplicated()会返回一个布尔型...