'Princi','Gaurav','Anuj'],'Age':[27,24,22,32],'Address':['Delhi','Kanpur','Allahabad','Kannauj'],'Qualification':['Msc','MA','MCA','Phd']}# Convert the dictionary into DataFramedf=pd.DataFrame(data)# select two columnsdf[['Name','Qualification']]...
Pandas DataFrame选择多个列范围时可以使用哪些操作符? ,可以使用切片(slicing)操作来实现。 切片操作可以通过指定列名的范围来选择多个列。以下是一个示例代码: 代码语言:python 代码运行次数:0 复制 importpandasaspd# 创建一个示例DataFramedata={'A':[1,2,3,4,5],'B':[6,7,8,9,10],'C':[11,12,13...
df.drop(df.columns[[0]], axis=1, inplace=True) Run Code Online (Sandbox Code Playgroud) 有一个可选参数,inplace以便可以在不创建副本的情况下修改原始数据. 膨化 列选择,添加,删除 删除列column-name: df.pop('column-name') Run Code Online (Sandbox Code Playgroud) 例子: df = DataFrame....
Columns are the different fields that contain their particular values when we create a DataFrame. We can perform certain operations on both rows & column values. DataFrames are 2-dimensional data structures in pandas. DataFrames consists of rows, columns, and the data. DataFrame can be created ...
Creating aDataFrameby passing a numpy array, with a datetime index and labeled columns: In [6]:dates=pd.date_range('20130101',periods=6)In [7]:datesOut[7]:DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04','2013-01-05', '2013-01-06'],dtype='datetime64...
df=pd.DataFrame(player_list,columns=['Name','Age','Weight','Salary']) # data frame before slicing df 输出: Python3实现 # Slicing columnss in data frame df1=df.iloc[:,0:2] # data frame after slicing df1 输出: 在上面的示例中,我们从dataframe中分割列。
df=pd.DataFrame(data) # select all rows # and second to fourth column df[df.columns[1:4]] 输出: 方法#2:使用loc[] 示例1:选择两列 # Import pandas package importpandasaspd # Define a dictionary containing employee data data={'Name':['Jai','Princi','Gaurav','Anuj'], ...
除了数据会duplicate,label也会出现duplicate的现象,这将导致一些问题。Series.reindex()函数处理duplicate index时出现错误。同时,使用index, columns对DataFrame进行slicing时会获取所有符合条件的数据。 通过Index.is_unique函数可以判断是否为unique的index,但该操作对于大数据集开销大,结果会被缓存。Index.duplicated()函数...
4.columns--- 查看DataFrame对象的列标签 5.values--- 查看对象的值 6.dtypes--- 查看数据类型 7.describe()--- 查看每列数据的描述统计量 五.索引(Indexing)和切片(Slicing) 1.data.loc[row_index_name, : ]--- 显式选取某行 2.data.iloc[row_index_from_zero, : ]--- 隐式选取某行 ...
print(x1,"\n")# Index slicing on Salary columnx2 = data.ix[10:20,'Salary'] print(x2) 输出: 代码3: # importing pandas and numpyimportpandasaspdimportnumpyasnp df = pd.DataFrame(np.random.randn(10,4), columns = ['A','B','C','D']) ...