pd.Series([2,3], index=['b', 'c'])]df = pd.DataFrame(l)print(df)print()# 有缺失值时删除列# 对第三行进行处理# 在原表上进行修改,不在原表上进行修改会返回修改后的新表# 有缺失值就进行删除print(df.dropna(axis=1, subset=[2], inplace=True, how='any'))print()print(df) 3.24 ...
DataFrame.duplicated([subset, keep]) Return boolean Series denoting duplicate rows, optionally only DataFrame.equals(other) 两个数据框是否相同 DataFrame.filter([items, like, regex, axis]) 过滤特定的子数据框 DataFrame.first(offset) Convenience method for subsetting initial periods of time series data...
For DataFrame label-indexing on the rows(行列同时索引的神器), I introduce the the special indexing operators loc and iloc. The enable you to select a subset of the rows and columns from a DataFrame with NumPy-like notaion using either axis lables(loc) or integers(iloc) As a preliminary(初...
to perform column-wise combine with another dateframe. func: merge function taking two arguments from the coresponding two dataframes. .combine_first(other) combine with a non-null-value merge function. reindex(columns=) filter and reorder columns. drop_duplicates(subset=[], keep='first'|'last...
df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False) axis= 0 按行检查缺失;1 按列检查缺失。不写默认为0 how= 'any' 有一个缺失值就算缺失;'all' 行或列(根据axis参数)全缺失才算缺失。不写默认为'any' thresh= x,x为一个整数,含义为行或列(根据axis参数)中非缺失数值个数...
frame.duplicated --- 0 False 1 False 2 False dtype: bool --- 上面提到 duplicated 返回布尔值,所以如果要想输出这些重复值,还需要和查询的方法配合使用 df[df.duplicated] ,比如: # 1、按user变量筛选重复值 frame[frame.duplicated(subset=['user'])] --- user price hobby ...
frame[frame.duplicated(subset=['user'])] --- user price hobby 1 zszxz 200 reading --- 1. 2. 3. 4. 5. 6. 上面按user一个变量进行查重,但没有设置keep参数,所以默认筛选出除了第一个以外的其它重复值。 AI检测代码解析 # 2、按user变量筛选重复值,保留全部...
df.dropna(axis=0, how='any', thresh=None, subset=None, inplace=False) axis= 0 按行检查缺失;1 按列检查缺失。不写默认为0 how= 'any' 有一个缺失值就算缺失;'all' 行或列(根据axis参数)全缺失才算缺失。不写默认为'any' thresh= x,x为一个整数,含义为行或列(根据axis参数)中非缺失数值个数...
DataFrame.duplicated([subset, keep])Return boolean Series denoting duplicate rows, optionally only DataFrame.equals(other)两个数据框是否相同 DataFrame.filter([items, like, regex, axis])过滤特定的子数据框 DataFrame.first(offset)Convenience method for subsetting initial periods of time series data based...
thresh: 设置删除行或者列的数量 subset:指定删除的行/列''' dropna(axis=0,how='any',thresh=3,subset=['总分','所在省份','名次']) 3.数据查重.duplicated()和重复行删除.drop_duplicates() 3.0. 新增 dup 列 标识出 重复行 PS.只有 第二次出现的才会被标识出来...