如果给定一个元组 (Sofa, 5000, 20) 并在 DataFrame 中找到它,可以这样完成: # if condition with row tuple given df[(df['Product']=='Sofa')&(df['MRP']==5000)&(df['Discount']==20)] 输出: 示例3:使用 Lambda 函数:Lambda 函数接受输入并根据特定条件返回结果。它可用于对 Pandas DataFrame 中...
创建数据框列表：
df = pd.DataFrame(data) # 定义条件和对应的值 conditions = [df['分数'] >= 90, (df['分数'] >= 80) & (df['分数'] < 90), df['分数'] < 80] values = ['优秀', '良好', '及格'] # 使用 case_when() 方法创建新列,并设置默认值为 '未知' df['等级'] = df['分数'].case_w...
After running the previous syntax the pandas DataFrame shown in Table 4 has been created. This time, we have kept all rows where the column x3 contains the values 1 or 3. Example 4: Extract Rows Based On Multiple Columns So far, we have specified our logical conditions only for one varia...
DataFrame.convert_dtypes() Series.convert_dtypes() 数据结构集成 一个Series、Index或DataFrame的列可以直接由一个类似于 NumPy 数组的pyarrow.ChunkedArray支持,要从主要的 pandas���据结构构造这些对象,您可以在类型后面加上[pyarrow]的字符串,例如"int64[pyarrow]"传递给dtype参数 代码语言:javascript 复...
importpandasaspd# 创建示例数据集data={'学生姓名':['Alice','Bob','Charlie','David','Eva'],'分数':[85,70,95,60,None]}df=pd.DataFrame(data)# 定义条件和对应的值conditions=[df['分数']>=90,(df['分数']>=80)&(df['分数']<90),df['分数']<80]values=['优秀','良好','及格']# ...
['Votes']>100000)]# Multiple conditions: OR - dataframe with all movies rated greater than 8 or having a metascore more than 90Or_df=df[(df['Rating']>8)|(df['Metascore']>80)]# Multiple conditions: NOT - dataframe with all emovies rated greater than 8 or having a metascore more ...
例如,pandas.read_hdf() 需要pytables 包,而 DataFrame.to_markdown() 需要tabulate 包。如果未安装可选依赖项,则在调用需要该依赖项的方法时,pandas 将引发 ImportError。 如果使用 pip,可选的 pandas 依赖项可以作为可选额外项(例如 pandas[performance, aws])安装或在文件中管理(例如 requirements.txt 或 ...
Comparison operators allow us to create conditions to filter our data as needed. Let’s see how these operators function within the realm of Pandas. For a sample DataFrame: import pandas as pd data = { 'Grade': [85, 90, 78, 88, 76, 95, 89] ...
# Create DataFrame with None/Null to work with examples import pandas as pd import numpy as np technologies = ({ 'Courses':["Spark","PySpark","Hadoop","Python","Pandas",None,"Spark","Python"], 'Fee' :[22000,25000,23000,24000,np.nan,25000,25000,22000], ...