Python Pandas：向数据框添加新列，这是索引列的副本
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Pandas: Adding new column to dataframe which is a copy of the index column
提问by ValientProcess
I have a dataframe which I want to plot with matplotlib, but the index column is the time and I cannot plot it.
我有一个数据框，我想用 matplotlib 绘制它，但索引列是时间，我无法绘制它。
This is the dataframe (df3):
这是数据框（df3）：
but when I try the following:
但是当我尝试以下操作时：
plt.plot(df3['magnetic_mag mean'], df3['YYYY-MO-DD HH-MI-SS_SSS'], label='FDI')
I'm getting an error obviously:
我显然收到一个错误：
KeyError: 'YYYY-MO-DD HH-MI-SS_SSS'
So what I want to do is to add a new extra column to my dataframe (named 'Time) which is just a copy of the index column.
所以我想要做的是向我的数据框（名为“时间”）添加一个新的额外列，它只是索引列的副本。
How can I do it?
我该怎么做？
This is the entire code:
这是整个代码：
#Importing the csv file into df
df = pd.read_csv('university2.csv', sep=";", skiprows=1)
#Changing datetime
df['YYYY-MO-DD HH-MI-SS_SSS'] = pd.to_datetime(df['YYYY-MO-DD HH-MI-SS_SSS'],
format='%Y-%m-%d %H:%M:%S:%f')
#Set index from column
df = df.set_index('YYYY-MO-DD HH-MI-SS_SSS')
#Add Magnetic Magnitude Column
df['magnetic_mag'] = np.sqrt(df['MAGNETIC FIELD X (μT)']**2 + df['MAGNETIC FIELD Y (μT)']**2 + df['MAGNETIC FIELD Z (μT)']**2)
#Subtract Earth's Average Magnetic Field from 'magnetic_mag'
df['magnetic_mag'] = df['magnetic_mag'] - 30
#Copy interesting values
df2 = df[[ 'ATMOSPHERIC PRESSURE (hPa)',
'TEMPERATURE (C)', 'magnetic_mag']].copy()
#Hourly Average and Standard Deviation for interesting values
df3 = df2.resample('H').agg(['mean','std'])
df3.columns = [' '.join(col) for col in df3.columns]
df3.reset_index()
plt.plot(df3['magnetic_mag mean'], df3['YYYY-MO-DD HH-MI-SS_SSS'], label='FDI')
Thank you !!
谢谢 ！！
回答by jezrael
I think you need reset_index:
我认为你需要reset_index：
df3 = df3.reset_index()
Possible solution, but I think inplaceis not good practice, check thisand this:
可能的解决方案，但我认为inplace这不是一个好习惯，检查这个和这个：
df3.reset_index(inplace=True)
But if you need new column, use:
但如果您需要新列，请使用：
df3['new'] = df3.index
I think you can read_csvbetter:
我认为你可以read_csv更好：
df = pd.read_csv('university2.csv',
sep=";",
skiprows=1,
index_col='YYYY-MO-DD HH-MI-SS_SSS',
parse_dates='YYYY-MO-DD HH-MI-SS_SSS') #if doesnt work, use pd.to_datetime
And then omit:
然后省略：
#Changing datetime
df['YYYY-MO-DD HH-MI-SS_SSS'] = pd.to_datetime(df['YYYY-MO-DD HH-MI-SS_SSS'],
format='%Y-%m-%d %H:%M:%S:%f')
#Set index from column
df = df.set_index('YYYY-MO-DD HH-MI-SS_SSS')
回答by Abbas
You can directly access in the index and get it plotted, following is an example:
您可以直接在索引中访问并绘制它，以下是一个示例：
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))
#Get index in horizontal axis
plt.plot(df.index, df[0])
plt.show()
#Get index in vertiacal axis
plt.plot(df[0], df.index)
plt.show()
