# Remove rows containing 'apple' in any column df = df[~df.apply(lambda row: row.astype(str).str.contains('apple')).any(axis=1)] print(df) 这将删除 DataFrame 任何列中包含字符串“apple”的所有行。您可以调整 lambda 函数中的条件以
For instance, first usesreplace()to replace infinite values with NaN, and thendropna()is applied to remove rows containing NaN. The resulting DataFrame (df) will not have rows containing infinite values. # Replace to drop rows or columns infinite values df = df.replace([np.inf, -np.inf],...
Remove all rows with NULL values: import pandas as pddf = pd.read_csv('data.csv')df.dropna(inplace = True) print(df.to_string()) Try it Yourself » Note: Now, the dropna(inplace = True) will NOT return a new DataFrame, but it will remove all rows containing NULL values from...
None/NaN values are one of the major problems in Data Analysis hence before we process either you need to remove rows that have NaN values orreplace NaN with empty for Stringandreplace NaN with zero for numeric columns. In this article, I will explain how to remove a row and column with...
import pandas as pd # Create a DataFrame with duplicate values data = {'Name': ['Alice', 'Bob', 'Charlie', 'Bob', 'Eva'], 'Age': [25, 30, 35, 30, 45]} df = pd.DataFrame(data) # Remove duplicate rows df_unique = df.drop_duplicates() print(df_unique) Output: 40. Show...
If we want to remove rows with only NaN values, we may also use notna function… data3b=data[data.notna().any(axis=1)]# Apply notna() functionprint(data3b)# Print updated DataFrame …or the notnull function: data3c=data[data.notnull().any(axis=1)]# Apply notnull() functionprint(...
pandas 删除对空的字符串使用concat()时的警告您可以使用所有可能的列名创建Index，然后重新索引最终的对象...
How to remove rows containing infinite values from a pandas DataFrame in Python - 2 Python programming examples - Python tutorial
Remove missing values. dropna(axis=0, how='any', thresh=None, subset=None, inplace=False) axis : {0 or 'index', 1 or 'columns'}, default 0 Determine if rows or columns which contain missing values are removed. * 0, or 'index' : Drop rows which contain missing values. ...
Level dtypes on Indexes from Series.sparse.from_coo() are now of dtype int32, the same as they are on the rows/cols on a scipy sparse matrix. Previously they were of dtype int64 first part of v2.0 changes #632 Index cannot be instantiated using a float16 dtype. Previously instantiating...