# 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_
复制 In [53]: df = pd.DataFrame({"AAA": [1, 2, 1, 3], "BBB": [1, 1, 2, 2], "CCC": [2, 1, 3, 1]}) In [54]: df Out[54]: AAA BBB CCC 0 1 1 2 1 2 1 1 2 1 2 3 3 3 2 1 In [55]: source_cols = df.columns # Or some subset would work too In [...
Python program to split column into multiple columns by comma # Importing pandas packageimportpandasaspd# Creating two dictionaryd={'Name':['Ram,Sharma','Shyam,rawat','Seeta,phoghat','Geeta,phogat'],'Age':[20,32,33,19] }# Creating a DataFramedf=pd.DataFrame(d)# Display DataFramesprint(...
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 2.Dataframe转化为字典数据 方法:DataFrame.to_dict(orient='dict', into=<class 'dict'>) !! orient可选参数有:‘dict’, ‘list’, ‘series’, ‘split’, ...
columns:列标签。如果没有传入索引参数,则默认会自动创建一个从0-N的整数索引。 通过已有数据创建 举例一: pd.DataFrame(np.random.randn(2,3)) 结果: 举例二:创建学生成绩表 使用np创建的数组显示方式,比较两者的区别。 # 生成10名同学,5门功课的数据 score = np.random.randint(40, 100, (10, 5))#...
1、一个array字段纵向扩展(多行) explode(col) 2、一个array字段横向扩展(多列) .str.split(,expand=True) 3、行转列(某些字段值转换为表头) pd.pivot 4、列转行(部分列名转换位一列数据值)pd.melt 5、多列合并两列(列合并)pd.lreshape HSql 行列转换(collect_list/set, lateral view + explode/posexpl...
time python -c "import pandas as pd; from scipy import array, concatenate; df = pd.DataFrame(['a b c']*100000, columns=['col']); print pd.DataFrame(concatenate(df['col'].apply( lambda x : [x.split(' ')]))).head()" Run Code Online (Sandbox Code Playgroud) ... 还有这个:...
PySpark split() Column into Multiple Columns Split the column of DataFrame into two columns How to Unpivot DataFrame in Pandas? Pandas Groupby Aggregate Explained Pandas GroupBy Multiple Columns Explained Pandas Groupby Sort within Groups Spark split() function to convert string to Array column ...
'values' : just the values array 表现效果如下: In [27]: df Out[27]: col1 col2 row1 1 0.50 row2 2 0.75 In [28]: df.to_json(orient='split') Out[28]: '{"columns":["col1","col2"],"index":["row1","row2"],"data":[[1,0.5],[2,0.75]]}' ...
* Series: - default is 'index' - allowed values are: {'split', 'records', 'index', 'table'}. * DataFrame: - default is 'columns' - allowed values are: {'split', 'records', 'index', 'columns', 'values', 'table'}. * The format of the JSON string: - 'split' : dict li...