pandas ：在python中删除编码为0的行 [英] Pandas: Delete rows with different encoding of 0s in python

问题描述

我已经计算了统计值，并将其写入了csv文件。 nan值将替换为零。有只有零的行，有只有0和0.0值的行。如何删除这些行？根据所附的图像行编号5，6（仅0.0s），9和11（均为0s和0.0s）需要删除。

I have calculated statistical values and written them to a csv file. The nan values are replaced with zeros. There are rows with only zeros and there are rows with both 0 and 0.0 values only. How can I delete these rows? According to the attached image rows number 5 , 6 (only 0.0s), 9 and 11 (both 0s and 0.0s) needs to get deleted.

import pandas as pd
all_df = pd.read_csv('source.csv')
all_df.dropna(subset=df_all.columns.tolist()[1:], how='all', inplace=True)
all_df.fillna(0, inplace=True)
all_df.to_csv('outfile.csv', index=False)

推荐答案

使用 all_df [（all_df.T！= 0）.any（）] 或 all_df [（all_df！= 0）.any（axis = 1）] ：

Use all_df[(all_df.T != 0).any()] or all_df[(all_df != 0).any(axis=1)]:

all_df = pd.DataFrame({'a':[0,0,0,1], 'b':[0,0,0,1]})
print all_df

   a  b
0  0  0
1  0  0
2  0  0
3  1  1

all_df = all_df[(all_df.T != 0).any()]
all_df

   a  b
3  1  1

编辑1 ：之后查看数据，一种解决方案是将所有数字列转换为浮点，然后执行操作。此问题来自将初始数据保存到.csv文件的方式。

EDIT 1: After looking at your data, a solution is to convert all numerical columns to float and then do the operations. This problem arises from the way the initial data were saved into the .csv file.

all_df = pd.read_csv('/Users/me/Downloads/Test11.csv')

# do not select 'activity' column
df = all_df.loc[:, all_df.columns != 'activity']

# convert to float
df = df.astype(float)

# remove columns with all 0s
mask = (df != 0).any(axis=1)
df = df[mask]

#mask activity column
recover_lines_of_activity_column = all_df['activity'][mask]

# Final result
final_df = pd.concat([recover_lines_of_activity_column, df], axis = 1)    

输出：

这篇关于 pandas ：在python中删除编码为0的行的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！