# Applying the condition df["gender"]=np.where(df["gender"]=="female",0,1) 输出: 方法3:使用pandas屏蔽函数 Pandas 掩码功能用于用条件替换任何行或列的值。现在使用这个掩码条件,我们将在性别列中将所有“女性”更改为 0。 语法:df['column_name'].mask( df['column_name'] == 'some_value', ...
The following code demonstrates how to exchange cells in a pandas DataFrame according to a logical condition. The Python code below replaces all values that are smaller or equal to 2 in the column x1 by the value 999: After running the previous Python programming code the pandas DataFrame illu...
2. Replace Single Value with a New Value in Pandas DataFrame If you want to replace a single value with a new value in a Pandas DataFrame, you can use thereplace()method. For instance, the replaces the value ‘Spark’ in the ‘Courses’ column with ‘Pyspark’. The resulting DataFrame ...
Related:You can replace the Pandas values based on condition. 1. replace() Syntax Below is the syntax of the replace() method. This is also used toreplace the substringin the column. # Syntax of replace() methodDataFrame.replace(to_replace=None,value=None,inplace=False,limit=None,regex=Fa...
28、使用replace进行值替换 df.replace({'OldValue': 'NewValue'}) df.replace({'Active': 'Active...
"""Pandas replace operation http://goo.gl/DJphs""" df[2].replace(4, 17, inplace=True) df[1][df[1] == 4] = 19 map操作 代码语言:python 代码运行次数:0 运行 AI代码解释 """apply and map examples""" """add 1 to every element""" df.applymap(lambda x: x+1) 第3行+2 代码...
y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index positionnp.where(y>5)array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition,# second will replace the values t...
condition# 需要多次str处理的链式操作#原因:每次调用函数,都会返回一个新的seriesdf['ymd'].str.replace('-','').slice(0,6)#'Series' object has no attribute 'slice'---意思就是series不能够直接去调用slice函数,必须经过str调用后才可以使用df['ymd'].str.replace('-','').str.slice(0,6)--这样...
# 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。 # create a list of our conditions conditions ...
df.sort_values() sort by column name df.sort_index() sort by index df.drop() delete columns df.set_index() set certain column as index df.reindex() change order of columns df.replace() replace values df.loc()/df.iloc() df.append() add rows ...