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
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....
值得注意的是,如果因为引入NaN而导致原始数据类型(如整数)无法表示NaN,Pandas会自动将该Series的数据类型(dtype)提升为可以容纳NaN的类型(通常是float64)。 从标量值创建:如果传递给pd.Series()的数据是一个单一的标量值(如一个数字或字符串),那么必须同时提供index参数。Pandas会将这个标量值重复广播,以匹配所提供...
(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...
print("Create DataFrame:\n", df) Yields below output. 4. Split String Column into Two Columns in Pandas Apply PandasSeries.str.split()on a given DataFrame column to split into multiple columns where the column has delimited string values. Here, I specified the'_'(underscore) delimiter betwee...
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 ...
df['A_squared']=df['A'].apply(square)print(df['A_squared'])Output:011429 使用.apply()将平方函数应用于整个'A'列。不需要显式循环。 3、条件操作 也将矢量化用于条件操作,比如基于列a中的条件创建一个新的列D: 代码语言:javascript 代码运行次数:0 ...
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...
#apply()函数使用案例# # 导入 numpy 库 import numpy as np # 导入 pandas 库 import pandas as pd # 定义 DataFrame # 数据为 3 行 4 列 s_data = pd.DataFrame([[5.1,3.5,1.4,0.2], [6.1,3.7,4.1,1.5], [5.8,2.7,5.1,1.9]], columns=['feature_one','feature_two','feature_three','fea...
s2=df1.apply(lambdax:x['D']+'_G',axis=1) s2.name='G' 合并1个DataFrame和2个Series: pd.concat([df1,s1,s2],axis=1) 合并后的结果: concat的要合并的对象参数可以只包含Series列表 合并两个Series: pd.concat([s1,s2],axis=1) 合并后的结果: ...