If you wanted to split a column of delimited strings rather than lists, you could similarly do: pd.DataFrame(df["teams"].str.split('<delim>', expand=True).values, columns=['team1','team2']) Share Copy link Improve this answer
3 Splitting rows into multiple columns in Pandas 3 Splitting a Pandas series and Assign Them into Separate Columns 1 Split columns into rows in Pandas 0 Dataframe split row data into columns 1 Split a dataframe column having a pandas series into multiple columns 1 How to split column ...
# 进行字符串分割 temp_list = [i.split(",") for i in df["Genre"]] # 获取电影的分类 genre_list = np.unique([i for j in temp_list for i in j]) # 增加新的列,创建全为0的dataframe temp_df = pd.DataFrame(np.zeros([df.shape[0],genre_list.shape[0]]),columns=genre_list) 2...
df.index.values和df.columns.values:->Series df.index.tolist()和df.columns.tolist():->list;Note:Index和Series对象都有tolist()方法 df.dtypes->series 返回每列的数据类型 df.info()可以理解成上面各个属性或者函数的集成,提供了有关数据的基本信息摘要。 df.describe()->DataFrame:可以返回dataframe中的...
Here, we are going to learn how to split column into multiple columns by comma in Python pandas?
分组(Split):根据指定的列(或多个列)将数据分成不同的组。 应用(Apply):对每个组应用一个函数(如求和、均值、计数等)。 合并(Combine):将结果合并成一个新的DataFrame或Series。 2.创建示例数据 为了演示groupby的用法,我们首先创建一个示例DataFrame: ...
import pandas as pd # 创建包含元组的DataFrame data = [(1, 'John', 25), (2, 'Alice', 30), (3, 'Bob', 35)] df = pd.DataFrame(data, columns=['ID', 'Name', 'Age']) # 定义函数将元组拆分成多列 def split_tuple(row): return pd.Series(row) # 使用apply函数将元组拆分成多列 ...
In [18]: pd.DataFrame.from_dict(data, orient='columns') Out[18]: key1 key2 key3 key4 key5 a -2 11 -34 8 46 b 100 1000 800 1100 400 方法:DataFrame.to_dict(orient='dict', into=<class 'dict'>) !! orient可选参数有:‘dict’, ‘list’, ‘series’, ‘split’, ‘records’...
"""convert a dictionary into a DataFrame"""make the keys into columns"""df=pd.DataFrame(dic,index=[0]) 转换字典类型为DataFrame,并且key转换成行数据 代码语言:python 代码运行次数:0 复制 Cloud Studio代码运行 """make the keys into row index"""df=pd.DataFrame.from_dict(dic,orient='index'...
df=DataFrame(np.random.randn(12).reshape((4,3)),columns=list("bde"),index=["Utah","Ohio","Texas","Oregon"])print("df:",df,sep='\n')print("pandas use numpy function result:",np.abs(df),sep='\n') 5.4.2 DataFrame对象的apply方法 ...