importpandasaspd# 创建一个示例DataFramedf=pd.DataFrame({'A':[1,2,3],'B':[4,5,6]})# 定义一个函数,计算两列的和defsum_two_columns(row):returnrow['A']+row['B']# 使用apply函数df['Sum']=df.apply(sum_two_columns,axis=1)print(df) Pyt
You can consolidate two or more columns of a DataFrame into a single column efficiently using theDataFrame.apply()function. This function is used to apply a function on a specific axis. When you concatenate two string columns using theapply()method, you can use ajoin() function to jointhis....
In Pandas, the apply() function can indeed be used to return multiple columns by returning a pandas Series or DataFrame from the applied function. In this article, I will explain how to return multiple columns from the pandas apply() function....
Python program to combine two columns with null values# Importing pandas package import pandas as pd # Importing numpy package import numpy as np # Creating two dictionary d = { 'A':['Raftar', 'Remo', None, None, 'Divine'], 'B':['Rap', None, 'Dance', None, None] } # Creating...
df['修改的列'] = df['条件列'].apply(调用函数名) import pandas as pd def test(): # 读取Excel文件 df = pd.read_excel('测试数据.xlsx') def modify_value(x): if x < 5: return '是' elif x < 10: return '否' else: return 'x' # 插入列 for col_num in range(4, 9): df....
```py In [61]: def mad(x): ...: return np.fabs(x - x.mean()).mean() ...: In [62]: s = pd.Series(range(10)) In [63]: s.rolling(window=4).apply(mad, raw=True) Out[63]: 0 NaN 1 NaN 2 NaN 3 1.0 4 1.0 5 1.0 6 1.0 7 1.0 8 1.0 9 1.0 dtype: float64 ``...
(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/...
iris_df.drop(columns='species', inplace=True) condition = iris_df['sepal_length'] >= 7 # 创建了一个布尔条件 condition数据帧 iris_df_filled = iris_df[condition] # 只包含"sepal_length"列大于等于7的行 实践中,一般更常用loc[ ]筛选满足条件的数据帧 ...
columns=['one','two','three','four']) data.drop(['Colorado','Ohio']) data.drop('two',axis=1) data.drop(['two','four'],axis=1) 四、索引、选取和过滤 DataFrame的索引: data = DataFrame(np.arange(16).reshape(4,4), index=['Ohio','Colorado','Utah','New York'], ...
df = pd.DataFrame([['A',1],['A',3],['A',2],['B',5],['B',9]], columns = ['name','score'])介绍两种高效地组内排序的方法。df.sort_values(['name','score'], ascending = [True,False])df.groupby('name').apply(lambda x: x.sort_values('score', ascending=False)).reset_...