示例2:使用lambda函数对多个列进行操作 importpandasaspd# 创建一个DataFramedf=pd.DataFrame({'A':[10,20,30],'B':[20,30,40],'C':['pandasdataframe.com','example','test']})# 使用lambda函数将两列数值相加df['A+B']=df.apply(lambdarow:row['A']+row['B'],axis=1)print(df) Python Copy...
'pandasdataframe.com','pandasdataframe.com']})# 使用 apply 和 lambda 来创建一个新列,根据条件修改值df['New Column']=df.apply(lambdarow:row['A']+row['B']ifrow['A']>150elserow['B'],axis=1)print(df)
# 复杂操作(Apply) start = time.time() pdf['price_category'] = pdf['price'].apply(lambda x: 1 if x > 50 else 0) pandas_apply_time = time.time() - start start = time.time() gdf['price_category'] = gdf['price'].apply(lambda x: 1 if x > 50 else 0) cudf_apply_time = ...
Apply a lambda function to multiple columns in DataFrame using Dataframe apply() along with lambda and Numpy functions.# Apply function NumPy.square() to square the values of two rows 'A'and'B df2 = df.apply(lambda x: np.square(x) if x.name in ['A','B'] else x) print("After ...
Lambda including if, elif and else Pandas: Find percentile stats of a given column Count number of non-NaN entries in every column of Dataframe Access Index of Last Element in pandas DataFrame in Python Pandas: Create two new columns in a DataFrame with values calculated from a pre-e...
First let's create duplicate columns by: df.columns = ['Date','Date','Depth','Magnitude Type','Type','Magnitude'] df Copy A general solution which concatenates columns with duplicate names can be: df.groupby(df.columns, axis=1).agg(lambdax: x.apply(lambday:','.join([str(l)forliny...
(3) Using lambda and join df[['Date','Time']].agg(lambdax:','.join(x.values),axis=1).T Copy So let's see several useful examples on how to combine several columns into one with Pandas. Suppose you have data like: 1: Combine multiple columns using string concatenation ...
(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/...
(4, 9): df.insert(loc=col_num, column=f'列{col_num-3}', value=None) # 如果A列【学号】<10,则E列【列1】填写:是;否则填写:否, df['列1'] = df['学号'].apply(lambda x: '是' if x < 10 else '否') # 保存修改后的DataFrame到新的Excel文件 df.to_excel('结果.xlsx', index=...
3 Applying different functions to DataFrame columns By passing a dict toaggregateyou can apply a different aggregation to the columns of a DataFrame: In [94]: grouped.agg({"C": np.sum,"D":lambdax: np.std(x, ddof=1)}) Out[94]: ...