Python code to modify a subset of rows # Applying condition and modifying# the column valuedf.loc[df.A==0,'B']=np.nan# Display modified DataFrameprint("Modified DataFrame:\n",df) Output The output of the above program is: Python Pandas Programs »
Signature:df.style.highlight_quantile( subset: 'Subset | None' = None, color: 'str' = 'yellow', axis: 'Axis | None' = 0, q_left: 'float' = 0.0, q_right: 'float' = 1.0, interpolation: 'str' = 'linear', inclusive: 'str' = 'both', props: 'str | None' = None,) -> '...
import pandas as pd df = pd.DataFrame(pd.read_excel('test1.xlsx', engine='openpyxl')) print(df['area']) df.drop_duplicates(subset=['area'], inplace=True) print(df['area']) df.to_excel('test1.xlsx', index=False) 0 北京 1 南京 2 天津 3 东莞 4 广州 5 深圳 6 深圳 Name: ...
np.random.shuffle(df.Fruit)np.random.shuffle(df.Vegetable)np.random.shuffle(df.Animal)df.head(30)# filter sets fruitsInclude=['Apple','Banana','Grape']vegetablesExclude=['Asparagus','Broccoli']# subset1:All rows and columns where:#(fruitinfruitsInclude)AND(Vegetable notinvegetablesExlude)df....
多列选择 →新DataFrame subset = sales_data[['产品', '销量']] 按行选择(超级实用!) first_two = sales_data.iloc[:2] # 前两行 promo_items = sales_data[sales_data['促销']] # 所有促销商品 传说中的交叉选择 ✨ result = sales_data.loc['A03', '单价'] # 输出:8999 ...
dataframe 新增单列 assign方法 dataframe assign方法,返回一个新对象(副本),不影响旧dataframe对象 import pandas as pd df...= pd.DataFrame({ 'col_1': [0, 1, 2, 3], ...
~df['Vegetable'].isin(vegetablesExclude)] # subset2: All rows and columns where: # (fru...
livel Match simple index on level of MultiIndex; otherwise select subset of. copy 删除行,列数据根据Axis Dropping one or more entries from an axis is easy if you already hava an index array or list without those entries. As that can requier a bit of munging(操作) and set logic. The dro...
Pandas DataFrame - Exercises, Practice, Solution: Two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels.
example_2 = pd.DataFrame(data)example_2#任务二:加载数据集“train.csv”文件#使用相对路径加载,并展示前三行数据df = pd.read_csv('train.csv')df.head(3)#任务三:查看DataFrame数据的每列名称df.columns#任务四:查看“Cabin”这列数据的所有值