# select all rows and 0 to 2 columns print(df.ix[:,0:2]) 输出: 注:本文由VeryToolz翻译自How to select multiple columns in a pandas dataframe,非经特殊声明,文中代码和图片版权归原作者Rajput-Ji所有,本译文的传播和使用请遵循“署名-相同方式共享 4.0 国际 (CC BY-SA 4.0)”协议。
df.rename(columns={'Order Quantity' : 'Order_Quantity', "Customer Fname" : "Customer_Fname"}, inplace=True) # Using query for filtering rows with a single condition df.query('Order_Quantity > 3') # Using query for filtering rows with multiple conditions df.query('Order_Quantity > 3 ...
In this example, we have selected multiple columns from the dataframe using the column names and the loc attribute. Here, you can observe that the program selects all the columns from the column"Maths"to the column"Chemistry". Hence, if we want to select contiguous columns using the column ...
If you don't sort your columns, you get: UnsortedIndexError:'MultiIndex Slicing requires the index to be fully lexsorted tuple len (2), lexsort depth (0)' If you have multipleRHScolumns in the second level,allthose columns are returned. ...
Why is the trailing slice:across the columns required? This is because,loccan be used to select and slice along both axes (axis=0oraxis=1). Without explicitly making it clear which axis the slicing is to be done on, the operation becomes ambiguous. See the big red box in thedocumentatio...
# Multiple row and column selections using iloc and DataFrame 使用iloc和DataFrame选择多个行和列 data.iloc[0:5] # first five rows of dataframe 数据帧的前五行 data.iloc[:, 0:2] # first two columns of data frame with all rows 数据帧的前两列,所有行 ...
Selecting multiple columns To select multiple columns, you can pass a list of column names to the indexing operator. wine_four = wine_df[['fixed_acidity', 'volatile_acidity','citric_acid', 'residual_sugar']] Alternatively, you can assign all your columns to a list variable and pass that...
Wiht partial column indexing you can similarly selectgroups of columns: (使用部分列索引, 可以相应地使用列组) frame['Ohio'] A MultiIndex can be created by itself and then reused; the columns in the preceding DataFrame with level names could be created like this. ...
To find unique values in multiple columns, we will use thepandas.unique()method. This method traverses over DataFrame columns and returns those values whose occurrence is not more than 1 or we can say that whose occurrence is 1. Syntax: ...
In [144]: se Out[144]: 0 1.0 1 2.0 2 3.0 5 5.0 dtype: float64 通过.loc,可以在任一轴上扩展 DataFrame。 代码语言:javascript 复制 In [145]: dfi = pd.DataFrame(np.arange(6).reshape(3, 2), ...: columns=['A', 'B']) ...: In [...