删除多列 在进行数据分析时，并非所有的列都有用，用df.drop可以方便地删除你指定的列。def drop_multiple_col(col_names_list, df): ''' AIM -> Drop multiple columns based on their column names INPUT -> List of column names, df OUTPUT -> updated df with dropped columns ---...
方法2:删除多列 如果希望同时删除多个列,可以将多个列的索引以列表的形式传入drop方法中。例如: # 删除第一列和第三列(索引为0和2)df_dropped_multiple=df.drop(df.columns[[0,2]],axis=1)# 显示删除后的DataFrameprint("\n删除多列后的DataFrame:")print(df_dropped_multiple) 1. 2. 3. 4. 5. 6....
# 使用ix进行下表和名称组合做引 data.ix[0:4, ['open', 'close', 'high', 'low']] # 推荐使用loc和iloc来获取的方式 data.loc[data.index[0:4], ['open', 'close', 'high', 'low']] data.iloc[0:4, data.columns.get_indexer(['open', 'close', 'high', 'low'])] open close hig...
df = pd.read_excel("test.xlsx", dtype=str, keep_default_na='') df.drop(columns=['寄件地区'], inplace=True) 5、列表头改名(补充) 如下:将某列表头【到件地区】修改为【对方地区】 df = pd.read_excel("test.xlsx", dtype=str, keep_default_na='') df = df.rename(columns={'到件地区...
# 方法2:使用 drop 方法 df2 = df.drop(df.columns[-1], axis=1) # 方法3:使用 iloc df3 = df.iloc[:, :-1] # 方法4:使用 loc df4 = df.loc[:, df.columns[:-1]] # 方法5:使用 filter df5 = df.filter(regex="^(?!"+df.columns[-1]+"$).*") ...
columns combine combine_first compare convert_dtypes copy corr corrwith count cov cummax cummin cumprod cumsum describe diff div divide dot drop drop_duplicates droplevel dropna dtypes duplicated empty eq equals eval ewm expanding explode ffill fillna filter first first_valid_index flags floordiv from_...
columns)): uni(telcomobject.columns[i]) # 综合之前的结果来看,在六个变量中存在No internet service,即无互联网服务对客户流失率影响很小,这些客户不使用任何互联网产品,因此可以将No internet service 和 No 是一样的效果,可以使用 No 替代 No internet service # In[31]: telcomvar.replace(to_replace='...
RangeIndex: 303 entries, 0 to 302Data columns (total 14 columns):age 303 non-null int64sex 303 non-null int64cp 303 non-null int64trestbps 303 non-null int64chol 303 non-null int64fbs 303 non-null int64restecg 303 non-null int64thalach 303 non-null int64exang 303 non-null int64old...
axis : {0 or 'index', 1 or 'columns'}, or tuple/list thereof Pass tuple or list to drop on multiple axes how : {'any', 'all'} * any : if any NA values are present, drop that label * all : if all values are NA, drop that label thresh : int, default None int value : ...
@dlt.table(schema=""" id int COMMENT 'This is the customer ID', name string COMMENT 'This is the customer full name', region string, ssn string MASK catalog.schema.ssn_mask_fn USING COLUMNS (region) """, row_filter ="ROW FILTER catalog.schema.us_filter_fn ON (region, name)"defsal...