英文:
Change the zero values of multiple rows with mode in Pandas
问题
我想更改8列中的'0'值，使用众数方法。0不仅是这些列的值，还有其他值。我只想更改这些列中的0值为众数值。假设数据集如下：
col1 col2 col3 col4 col5 col6 col7 col8 col9 col10
我想一次性从col3到col10更改'0'值。
我使用以下代码将0更改为众数：
replace_col = df['col3']
replace_col.replace(to_replace=0, value=replace_col.mode()[0], inplace=True)
但是，我需要更改df的值，例如df['col3']，df['col4']，df['col4']每次。所以我需要运行8次代码来更改8列的值。是否有一种方法可以一次运行一个代码片段来更改一个值？
谢谢。
英文:
I have a dataset with 10 columns. Among them I want to change the '0' values of 8 columns with mode methods. 0 is not only the values of those columns, there are other values too. Among them I only want change the 0 values with mode. Suppose the dataset looks like this,
col1 col2 col3 col4 col5 col6 col7 col8 col9 col10
I want to change the '0' values from col3 to col10 at a time.
I used the following code to change 0 to mode
replace_col = df['col3']replace_col.replace(to_replace = 0, value = replace_col.mode()[0], inplace=True)
However, I need to change the values of df, such as df['col3'], df['col4'], df['col4'] each time. So I need to run the code 8 times to change the values of 8 columns. Is there any way I can change the value at a time running one code snippet?
Thank you.
答案1
得分: 1
import pandas as pd# 示例数据框data = {'col1': [1, 2, 3, 4, 5],'col2': [0, 6, 7, 0, 9],'col3': [10, 0, 0, 13, 14],'col4': [15, 0, 17, 18, 19],'col5': [0, 21, 22, 23, 0],'col6': [25, 0, 0, 28, 0],'col7': [30, 31, 0, 33, 0],'col8': [0, 35, 36, 0, 0],'col9': [40, 41, 42, 43, 44],'col10': [0, 51, 52, 53, 0]}df = pd.DataFrame(data)# 要更新的列cols_to_update = ['col3', 'col4', 'col5', 'col6', 'col7', 'col8', 'col9', 'col10']# 将每列中的0值替换为众数for col in cols_to_update:replace_col = df[col]mode_value = replace_col.mode()[0]replace_col.replace(to_replace=0, value=mode_value, inplace=True)print(df)
英文:
import pandas as pd# Sample DataFramedata = {'col1': [1, 2, 3, 4, 5],'col2': [0, 6, 7, 0, 9],'col3': [10, 0, 0, 13, 14],'col4': [15, 0, 17, 18, 19],'col5': [0, 21, 22, 23, 0],'col6': [25, 0, 0, 28, 0],'col7': [30, 31, 0, 33, 0],'col8': [0, 35, 36, 0, 0],'col9': [40, 41, 42, 43, 44],'col10': [0, 51, 52, 53, 0]}df = pd.DataFrame(data)# Columns to updatecols_to_update = ['col3', 'col4', 'col5', 'col6', 'col7', 'col8', 'col9', 'col10']# Replace 0 values with mode for each columnfor col in cols_to_update:replace_col = df[col]mode_value = replace_col.mode()[0]replace_col.replace(to_replace=0, value=mode_value, inplace=True)print(df)
答案2
得分: 1
在你的情况下执行：df = df.mask(df==0, df.mask(df==0).mode().loc[0], axis=1)
英文:
In your case do
df = df.mask(df==0,df.mask(df==0).mode().loc[0],axis=1)
答案3
得分: 1
这是我的答案：
import pandas as pd# 示例数据框data = {'col1': [1, 2, 3, 4, 5],'col2': [0, 6, 7, 0, 9],'col3': [10, 0, 0, 13, 14],'col4': [15, 0, 17, 18, 19],'col5': [0, 21, 22, 23, 0],'col6': [25, 0, 0, 28, 0],'col7': [30, 31, 0, 33, 0],'col8': [0, 35, 36, 0, 0],'col9': [40, 41, 42, 43, 44],'col10': [0, 51, 52, 53, 0]}df = pd.DataFrame(data)print(df)df1 = df.copy()cols = df.columns# 将0替换为999999df1[cols] = df1[cols].replace(0, 999999)print(df1)
输出结果如下：
col1 col2 col3 col4 col5 col6 col7 col8 col9 col100 1 0 10 15 0 25 30 0 40 01 2 6 0 0 21 0 31 35 41 512 3 7 0 17 22 0 0 36 42 523 4 0 13 18 23 28 33 0 43 534 5 9 14 19 0 0 0 0 44 0col1 col2 col3 col4 col5 col6 col7 col8 col9 col100 1 999999 10 15 999999 25 30 999999 40 9999991 2 6 999999 999999 21 999999 31 35 41 512 3 7 999999 17 22 999999 999999 36 42 523 4 999999 13 18 23 28 33 999999 43 534 5 9 14 19 999999 999999 999999 999999 44 999999
英文:
Here my answer:
import pandas as pd# Sample DataFramedata = {'col1': [1, 2, 3, 4, 5],'col2': [0, 6, 7, 0, 9],'col3': [10, 0, 0, 13, 14],'col4': [15, 0, 17, 18, 19],'col5': [0, 21, 22, 23, 0],'col6': [25, 0, 0, 28, 0],'col7': [30, 31, 0, 33, 0],'col8': [0, 35, 36, 0, 0],'col9': [40, 41, 42, 43, 44],'col10': [0, 51, 52, 53, 0]}df = pd.DataFrame(data)print(df)df1 = df.copy()cols = df.columns# inplace of 999999 you can put anythingdf1[cols] = df1[cols].replace(0, 999999)print(df1)col1 col2 col3 col4 col5 col6 col7 col8 col9 col100 1 999999 10 15 999999 25 30 999999 40 9999991 2 6 999999 999999 21 999999 31 35 41 512 3 7 999999 17 22 999999 999999 36 42 523 4 999999 13 18 23 28 33 999999 43 534 5 9 14 19 999999 999999 999999 999999 44 999999
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