pandasisin和notin的使⽤说明 简介 pandas按条件筛选数据时，除了使⽤query()⽅法，还可以使⽤isin和对isin取反进⾏条件筛选.代码 import pandas as pd df = pd.DataFrame({'a':[1, 2, 3, 4, 5, 6],'b':[1, 2, 3, 4, 5, 6],'c':[1, 2, 3, 4, 5, 6]})filter_condition ...
# SQL: sql = """ SELECT * FROM titanic where Sex='male' and Age>=20.0 and Age<=40.0 LIMIT 5; """ # 使用括号的方式,级联多个条件| condition = (df["Sex"]=="male") & (df["Age"]>=20.0) & (df["Age"]<=40.0) condition.value_counts() False 629 True 262 dtype: int64 df[co...
condition.value_counts() df[condition].head(5) 3. in和not in的条件查询 df['Pclass'].unique() # SQL: sql =''' SELECT * FROM titanic where Pclass in (1,2) LIMIT 5; ''' # in df[df['Pclass'].isin((1,2))].head() # not in df[~df['Pclass'].isin((1,2))].head() ...
pandas数据筛选 isin 和 ~ isin importpandasaspd data={'city':['Beijing','Shanghai','Guangzhou','Shenzhen','Hangzhou','Chongqing'],'year':[2016,2016,2015,2017,2016,2016],'population':[2100,2300,1000,700,500,500]}frame=pd.DataFrame(data,columns=['year','city','population','debt'])co...
df[condition].head(5) 1. 3. in和not in的条件查询 df["Pclass"].unique() 1. # SQL:sql = """ SELECT * FROM titanic where Pclass in (1,2) LIMIT 5;""" 1. # in df[df["Pclass"].isin((1,2))].head() 1. # not in df[~df["Pclass"].isin((1,2))].head() ...
for row in reader: # 根据条件判断是否删除该行 if not condition: rows_to_keep.append(row) 关闭CSV文件。 代码语言:txt 复制 file.close() 使用csv模块打开CSV文件,并创建一个写入器对象。 代码语言:txt 复制 with open('data.csv', 'w', newline='') as file: ...
What is 'NOT IN' Filter in Pandas?The "NOT IN" the filter is used to check whether a particular data is available in the DataFrame or not. The "NOT IN" the condition in pandas is checked by using the DataFrame.isin() operator.How...
Pandas NOT IN(~)operator filter is used to check whether a particular data is available in the DataFrame or not. Pandas don’t have a NOT IN operator, however, you can perform the NOT IN condition by negatingDataFrame.isin()result. In this article, I will explain how to filter with a...
np.where(condition1,x1,np.where(condition2,x2,np.where(condition3,x3,...))) 五、自定义函数规范列 接下来就要对列中的字符串进行整理,除了利用循环和.str()方法相结合的方式进行操作,我们还可以选择用applymap()方法,它会将传入的函数作用于整个DataFrame所有行列中的每个元素。
Notice the use of the bitwise NOT operator~. It inverts the boolean values returned bydf['Plan'].isin(['Basic', 'Premium']), giving us the rows that do not match the condition. Using NOT IN with Numerical Columns Suppose you want to filter out the rows whereMonthlyChargeis not 20 or...