condition = df['A'] > 2 df = df.drop(df[condition].columns, axis=1) 在上述代码中,我们首先定义一个条件(condition),即选择'A'列中大于2的行。然后使用drop()方法删除满足条件的列,指定axis=1表示按列删除。 最后,DataFrame(df)将只包含满足条件的列之外的列。
For example,Select rows with single condition with loc 1 2 3 4 5 6 7 import pandas as pd df = pd.DataFrame([['Jay','M',21],['Jennifer','F',17], ['Preity','F',19],['Neil','M',17]], columns = ['Name','Gender','Age']) print(df[df['Age']>18]) Output:...
[:, col_condition] 如果需要在列和行之间切换,只需使用 df.T 因此,对于非零数小于列长度1%的行也是如此: row_condition = df.T[df.T > 0].count() / df.shape[1] >= .01df_ = df[row_condition] 以更简洁的形式: df_ = df.loc[:, (~df.eq(0)).mean() >= .01] # keep columnsdf...
select[column1, column2 ... ]where[condition1, condition2 ... ] 最基本的 select 操作,其实在前面的电影筛选中已经介绍过了,比如: # 筛选电影排名小于等于 5 且评分高于 9.0printmovie_pd[(movie_pd['rank']<=5)&(movie_pd['score']>9.0)]# 筛选电影发布日期大于 2010-01-01 或 评论数超过 50...
[False, True, False, True, False, False, False, True, False, True, False, True])# Use extract to get the valuesnp.extract(cond, array)array([ 1, 19, 11, 13, 3])# Apply condition on extract directlynp.extract(((array < 3) | (array >...
您可以使用by关键字参数创建分组来创建分层箱线图。例如, In [48]: df = pd.DataFrame(np.random.rand(10, 2), columns=["Col1", "Col2"]) In [49]: df["X"] = pd.Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"]) In [50]: plt.figure(); In [51]...
In [53]: df.empty Out[53]: False In [54]: pd.DataFrame(columns=list("ABC")).empty Out[54]: True 警告 断言pandas 对象的真实性会引发错误,因为空值或值的测试是模棱两可的。 代码语言:javascript 代码运行次数:0 运行 复制 In [55]: if df: ...: print(True) ...: --- ValueError Tra...
#np.where(condition, value if condition is true, value if condition is false) df['hasimage'] = np.where(df['photos']!= '[]', True, False) 多条件:使用一个名为np.select()的函数,给它提供两个参数:一个是条件,另一个是对应的等级列表。
df.columns columns name, can be rewriten df.dtype df.iterrows() iterate over rows df.rename() rename the column name df.select_dtype(include='typename') select data by datatype df.sort_values() sort by column name df.sort_index() sort by index ...
Square brackets will return all the rows and wherever the condition is satisfied, it will return all the columns. Let us understand with the help of an example, Python program to select rows whose column value is null / None / nan