Trying to plot a rolling corr line chart but Matplot keeps saying to bring in only valid columns? - python-3.x

Question

Im trying to create a rolling corr using matplot but I get the error "select only valid columns before calling the operation. Dropped columns were Index(['time'], dtype='object')
I have dropped that field from my data frame but the error keeps on appearing ?
Is it something to do with my .iloc argument?
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import requests
import seaborn as sns
import scipy.stats as stats
import json
from datetime import timezone
from datetime import datetime
from pycoingecko import CoinGeckoAPI
pd.options.display.width = 0
def datetime_to_unix(year, month, day):
'''datetime_to_unix(2021, 6, 1) => 1622505600.0'''
dt = datetime(year, month, day)
timestamp = (dt - datetime(1970, 1, 1)).total_seconds()
return timestamp
def unix_to_datetime(unix_time):
'''unix_to_datetime(1622505700)=> ''2021-06-01 12:01am'''''
ts = int(unix_time/1000 if len(str(unix_time)) > 10 else unix_time) # /1000 handles milliseconds
return datetime.utcfromtimestamp(ts).strftime('%Y-%m-%d %l:%M%p').lower()
# Initialize the client
cg = CoinGeckoAPI()
# Retrieve looksrare data in USD
result = cg.get_coin_market_chart_range_by_id(
id='looksrare',
vs_currency='usd',
from_timestamp=datetime_to_unix(2022, 1, 11),
to_timestamp=datetime_to_unix(2022, 4, 20)
)
time = [ unix_to_datetime(i[0]) for i in result['prices'] ]
p_array = np.array(result['prices'])
price = p_array[:,1]
v_array = np.array(result['total_volumes'])
volume = v_array[:,1]
df = pd.DataFrame({'time':time, 'price':price,})
df.head(100)
# Retrieve ETH data in USD
result = cg.get_coin_market_chart_range_by_id(
id='ethereum',
vs_currency='usd',
from_timestamp=datetime_to_unix(2022, 1, 11),
to_timestamp=datetime_to_unix(2022, 4, 20)
)
time = [ unix_to_datetime(i[0]) for i in result['prices'] ]
p_array = np.array(result['prices'])
price = p_array[:,1]
v_array = np.array(result['total_volumes'])
volume = v_array[:,1]
df2 = pd.DataFrame({'time':time, 'price':price,})
df2.head(100)
df_cd = pd.merge(df, df2, how='inner', on='time')
df_cd = df_cd.drop('time', 1)
output = df_cd.corr()
output1 = df_cd['price_x'].corr(df_cd['price_y'])
overall_pearson_r = df_cd.corr().iloc[0,1]
print(df_cd)
print(f"Pandas computed Pearson r: {overall_pearson_r}")
r, p = stats.pearsonr(df_cd.dropna()['price_x'], df_cd.dropna()['price_y'])
print(f"Scipy computed Pearson r: {r} and p-value: {p}")
# compute rolling window synchrony
f,ax=plt.subplots(figsize=(7,3))
df.rolling(window=30,center=True).median().plot(ax=ax)
ax.set(xlabel='Time',ylabel='Pearson r')
ax.set(title=f"Overall Pearson r = {np.round(overall_pearson_r,2)}");