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) Python Copy Output: 示例代码2:根据条件创建新列 ...
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....
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 between the string values of one of the columns (which we want to split into two columns) of our DataFrame. ...
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...
```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 ``...
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....
(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/...
如果我们想要将第二列扩展成DataFrame，我们可以对那一列使用apply()函数并传递给Series constructor:通过...
如果我们想要将第二列扩展成DataFrame,我们可以对那一列使用 apply 函数并传递给Series constructor: df_new = df.col_two.apply(pd.Series) df_new 通过使用 concat 函数,我们可以将原来的DataFrame和新的DataFrame组合起来: pd.concat([df, df_new], axis='columns') ...
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'], ...