KeyError: 'Date' when it's spelled correctly

Question

I have a program that reads a datarframe and does a line graph on its data, I'm trying to overlay a scatter of another dataframe in the same graph, but I'm getting the error KeyError: 'Date on the line ax3 = TMIN2015.plot.scatter(x ='Date', y='TMIN',secondary_y=True, ax=ax1) I'll add the code and how the dataframe csv file originally looks like

import matplotlib.pyplot as plt
import mplleaflet
import pandas as pd

import numpy as np

def leaflet_plot_stations(binsize, hashid):

    df = pd.read_csv('data/C2A2_data/BinSize_d{}.csv'.format(binsize))

    station_locations_by_hash = df[df['hash'] == hashid]

    lons = station_locations_by_hash['LONGITUDE'].tolist()
    lats = station_locations_by_hash['LATITUDE'].tolist()

    #plt.figure(figsize=(8,8))

    #plt.scatter(lons, lats, c='r', alpha=0.7, s=200)
    
    #---my code---
    #1. Read the documentation and familiarize yourself with the dataset, then write some python code which returns a line graph 
    #of the record high and record low temperatures by day of the year over the period 2005-2014. The area between the record 
    #high and record low temperatures for each day should be shaded.
    
    df2 = pd.read_csv('data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv')
    df2['Date'] = pd.to_datetime(df2['Date'])
    
    df2[(df2['Date'] > '2005-01-01') & (df2['Date'] < '2014-12-31')]
    #sorting by date to make the data reading easier
    df2 = df2.sort_values(by="Date")
    
    #groupyingby the min and max
    df2 = df2.groupby('Date')['Data_Value'].agg([('TMIN','min'), ('TMAX','max')])
    
    #this other syntaxis is valid, but due to the version of the current library it causes an error, that's why I used the 
    #method above
    
    #df2.groupby('Date').agg(TMIN = ('Data_Value', 'min'), TMAX = ('Data_Value', 'max'))
    
    #reseting the index so I can use the dates further on
    df2 = df2.reset_index()
    
    #creating the line graph
    ax1 = df2.plot(x ='Date', y='TMIN', kind = 'line')
    ax2 = df2.plot(x ='Date', y='TMAX',secondary_y=True, ax=ax1)

    y1 = df2['TMIN']
    y2 = df2['TMAX']
    
    #to calculate the position in scale
    ny = ((y2-np.min(y2))/(np.max(y2)-np.min(y2)))*(np.max(y1)-np.min(y1))+np.min(y1)
    
    #shading between spaces
    #ax1.fill_between(df2['Date'],y1,ny, where=y1<=ny, interpolate=True, color='grey', alpha=0.5)
    #ax1.fill_between(df2['Date'],y1,ny, where=y1>=ny, interpolate=True, color='grey', alpha=0.5)
    plt.fill_between(df2['TMIN'],df2['TMAX'], interpolate=True, color='grey', alpha=0.5)
    
    plt.xlabel('Year Interval')
    plt.ylabel('Temperature Frequency')
    plt.title('Record high and record low temperatures by day of the year over the period 2005-2014')

    
    #2. Overlay a scatter of the 2015 data for any points (highs and lows) for which the ten year record (2005-2014) record 
    #high or record low was broken in 2015.
    
    TMIN2015 = pd.read_csv('data/C2A2_data/BinnedCsvs_d400/fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89.csv')
    TMIN2015['Date'] = pd.to_datetime(TMIN2015['Date'])
    TMIN2015 = TMIN2015[(TMIN2015['Date'] > '2015-01-01') & (TMIN2015['Date'] < '2015-12-31')]
    TMIN2015 = TMIN2015.sort_values(by="Date")
    TMIN2015 = TMIN2015.groupby('Date')['Data_Value'].agg([('TMIN','min'), ('TMAX','max')])
    TMIN2015 = TMIN2015.reset_index()
    
    ax3 = TMIN2015.plot.scatter(x ='Date', y='TMIN',secondary_y=True, ax=ax1)
    ax4 = TMIN2015.plot.scatter(x ='Date', y='TMAX',secondary_y=True, ax=ax1)
    
    return plt.show()

leaflet_plot_stations(400,'fb441e62df2d58994928907a91895ec62c2c42e6cd075c2700843b89')

An example on how the table looks like without being modified in code:

	ID	Date	Element	Data_Value
0	USW00094889	2014-11-12	TMAX	22
1	USC00208972	2009-04-29	TMIN	56
2	USC00200032	2008-05-26	TMAX	278