蟒蛇熊猫剧情警告

Question

当我在spyder中使用pandas绘图数据时。它将始终显示一条警告消息：

C:\ProgramData\Anaconda3\lib\site packages\pandas\core\index。py:494:SettingWithCopyWarning:试图在数据帧切片的副本上设置值。尝试使用。loc[row\u indexer，col\u indexer]=改为value。请参阅文档中的注意事项：http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-vs-a-copy self。obj[项目]=s

我的脚本如下：

import pandas as pd

loc0 = r'D:\DC_BP00010.T0058_20190804_082758_15_14_PM2_1031.txt'
loc1 = r'D:\DC_BP00010.T0058_20190804_082758_17_16_PM1_1193.txt'

locs = [loc0,loc1]  # Update files in the list

parameters_must_be = ['Time', 'StepNo (Int)'] # Do not change this one
parameters = ["HeadBEPTrend1 (Float)"] # Key in parameters needed
parameters_all = parameters_must_be.append(parameters) 

endpoint_steps = [3] # Update endpoint steps in the list

Titles = 'BP00010' # Titles for plot

Colors = ['black','red','brown','crimson','olive','blue','yellow','darkorange','lime','purple','deeppink','dodgerblue','orange','indigo','darkslateblue','lawngreen','darkslategray','darkgreen','midnightblue','lightseagreen','gold','maroon','navy','teal']

dfs = []
sa = []
ss = []
axs = []
n = len(locs) # Number of datalog files

for i in range(0,n):
    dfs.append(pd.read_csv(locs[i], sep=('\t'), skiprows=6, usecols=parameters_all))
    ss.append(dfs[i][dfs[i]['StepNo (Int)'].isin(endpoint_steps)])
    sa.append(ss[i].loc[:,'Time']-ss[i].loc[:,'Time'].iloc[0])
    ss[i].loc[:,'Time'] = sa[i].loc[:]

ax=ss[0].plot(x='Time', y=parameters, title=Titles, color=Colors[0:len(parameters)], linewidth=1)

if n>1:
    for i in range(1,n):
        axs.append(ss[i].plot(x='Time', y=parameters, color=Colors[len(parameters)*i:len(parameters)*i+len(parameters)], linewidth=1, ax=ax))

ax.set_xlim(0,80)
ax.set_ylim(0,60)
ax.set_ylabel('Endpoint Intensity')
ax.legend(['BP00010#14','BP00010#15','BP00010#16','BP00010#17'], bbox_to_anchor=(1.31, .5), loc=5, borderaxespad=0.)

Answer 1

• 您试图更改另一个数据框中的值。
• ss是一个数据帧列表
• ss[i]是来自dfs
的特定数据帧

• ss。追加（dfs[i][dfs[i][StepNo（Int）].isin（endpoint\u steps）]）

• sa来自ss

• ss。追加（dfs[i][dfs[i][StepNo（Int）”.isin（endpoint\u steps）].copy（））
• sa。追加（ss[i].loc[：，'Time']-ss[i].loc[：，'Time'].iloc[0].copy（））