for column in columns_to-check: outliers_dict[column] = find_outliers_pandas(df, column) # 打印每列中带有异常值的记录 for column, outliers in outliers_dict.items(): print(f"Outliers in '{column}':") print(outliers) print("\n") 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. ...
(self, key, value) 1284 ) 1285 1286 check_dict_or_set_indexers(key) 1287 key = com.apply_if_callable(key, self) -> 1288 cacher_needs_updating = self._check_is_chained_assignment_possible() 1289 1290 if key is Ellipsis: 1291 key = slice(None) ~/work/pandas/pandas/pandas/core/seri...
'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__...
# Check for missing values in the dataframedf.isnull()# Check the number of missing values in the dataframedf.isnull().sum().sort_values(ascending=False)# Check for missing values in the 'Customer Zipcode' columndf['Customer Zipcode'].isnull().sum()# Check what percentage of the data ...
fillna(df.median()) # Replace missing values in Order Quantity column with the mean of Order Quantities df['Order Quantity'].fillna(df["Order Quantity"].mean, inplace=True) 检查重复行 duplicate()方法可以查看重复的行。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # Check duplicate ...
# Check for missing values in the dataframe df.isnull() # Check the number of missing values in the dataframe df.isnull().sum().sort_values(ascending=False) # Check for missing values in the 'Customer Zipcode' column df['Customer Zipcode'].isnull().sum() # Check what percentage of ...
39. How do you check and remove duplicate values in Pandas? In Pandas, duplicate values can be checked by using the duplicated() method. DataFrame.duplicated() Here’s an example code: import pandas as pd # Create a DataFrame with duplicate values data = {'Name': ['Alice', 'Bob', '...
one-to-one joins: for example when joining twoDataFrameobjects on their indexes (which must contain unique values). many-to-one joins: for example when joining an index (unique) to one or more columns in a differentDataFrame. many-to-many joins: joining columns on columns. ...
# Calculating cumulative sumdf['Cumulative_Sum'] = df['Values'].cumsum() 13、删除重复的数据 # Removing duplicate rowsdf.drop_duplicates(subset=['Column1', 'Column2'], keep='first', inplace=True) 14、创建虚拟变量 pandas.get_dummies() 是 Pandas 中...
Thekeep='first'parameter in.duplicated()retains the first occurrence of each duplicate column, dropping subsequent duplicates. Setkeep='last'in.duplicated()to keep the last occurrence of each duplicate column while dropping earlier ones. Use.duplicated(subset=columns)to check for duplicates within a...