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Axis's Plot Show Wrong Output

I have this code:
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()
import yfinance as yf
df = yf.download('AAPL',
start='2001-01-01',
end='2005-12-31',
progress=False)
df.head()
df = df.reset_index()
df['Date'] = pd.to_datetime(df.Date, format='%Y%m%d')
df.dropna(how='any', inplace=True)
# Plot the returns
plt.figure(figsize=(10,6))
plt.grid(True)
plt.xlabel('Dates')
plt.ylabel('Prices')
plt.plot(df['Close'])
plt.title('Close Price', fontsize=16)
plt.show()
The output of the close price plot is
We can see that the dates and price didn't show correct output. I have checked the type of dataframe's date.
df.info()
The results is
I have tried some ways but it didn't work. How to solve this problem?

Don't reset the index. The index Dates is already a datetime.
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()
import yfinance as yf
df = yf.download('AAPL',
start='2001-01-01',
end='2005-12-31',
progress=False)
# df.head()
# df = df.reset_index() # <- DON'T DO THAT
# df['Date'] = pd.to_datetime(df.Date, format='%Y%m%d') # <- DON'T DO THAT
# df.dropna(how='any', inplace=True)
# Plot the returns
plt.figure(figsize=(10,6))
plt.grid(True)
plt.xlabel('Dates')
plt.ylabel('Prices')
plt.plot(df['Close'])
plt.title('Close Price', fontsize=16)
plt.show()
To modify the date axis, read Date tick labels from matplotlib documentation.

problem with inserting vertical lines in specific days in matplotlib date

I want to insert some vertical dashed lines in my plot. I use the following code, and I face the error " ValueError: view limit minimum -34758.04999999988 is less than 1 and is an invalid Matplotlib date value. This often happens if you pass a non-datetime value to an axis that has datetime units ". Here is a sample of my data.
Date M
2013-03-13 0.727195
2013-03-14 0.727195
2013-03-15 0.727195
2013-03-16 0.727195
2013-03-17 0.727195
... ...
2018-11-12 0.115674
2018-11-13 -0.427214
2018-11-14 -0.389715
2018-11-15 0.427149
2018-11-16 -0.416864
[2075 rows x 1 columns]
and this is my code
import pandas as pd
from datetime import datetime, timedelta
from matplotlib import pyplot as plt
from matplotlib import dates as mpl_dates
data=pd.read_excel('ff.xlsx')
data['Date']=pd.to_datetime(data['Date'], format="%Y-%m-%d")
date = data['Date']
amount = data['M']
data.set_index('Date', inplace=True, drop=True)
plt.plot(date,amount, color='blue')
ax = plt.axes()
ax.yaxis.grid()
plt.ylabel('dvv percentage')
xposition = [2015-11-11, 2014-11-11]
for xc in xposition:
plt.axvline(x=xc, color='k', linestyle='--')
plt.show()

this should work:
from matplotlib import pyplot as plt
import pandas as pd
# example df:
df = pd.DataFrame({'Date':['2013-03-13','2015-03-14','2019-03-15'],
'M':[0.727195, -0.727195, 0.669195]})
# ensure datetime:
df['Date'] = pd.to_datetime(df['Date'])
ax = plt.plot(df.Date, df.M, color='blue')
ax = plt.axes()
ax.yaxis.grid()
plt.ylabel('dvv percentage')
# vertical line position as datetime dtype:
xposition = pd.to_datetime(['2015-11-11', '2014-11-11'])
for xc in xposition:
ax.axvline(x=xc, color='k', linestyle='--')
plt.show()

Changing the tick frequency on the x-axis

I am trying to plot a bar chart with the date vs the price of a crypto currency from a dataframe and have 731 daily samples. When i plot the graph i get the image as seen below. Due to the amount of dates the x axis is unreadable and i would like to make it so it only labels the 1st of every month on the x-axis.
This is the graph i currently have: https://imgur.com/a/QVNn4Zp
I have tried using other methods i have found online both in stackoverflow and other sources such as youtube but had no success.
This is the Code i have so far to plot the bar chart.
df.plot(kind='bar',x='Date',y='Price in USD (at 00:00:00 UTC)',color='red')
plt.show()

One option is to plot a numeric barplot with matplotlib.
Matplotlib < 3.0
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
start = pd.to_datetime("5-1-2012")
idx = pd.date_range(start, periods= 365)
df = pd.DataFrame({'Date': idx, 'A':np.random.random(365)})
fig, ax = plt.subplots()
dates = mdates.date2num(df["Date"].values)
ax.bar(dates, df["A"], width=1)
loc = mdates.AutoDateLocator()
ax.xaxis.set_major_locator(loc)
ax.xaxis.set_major_formatter(mdates.AutoDateFormatter(loc))
plt.show()
Matplotlib >= 3.0
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
pd.plotting.register_matplotlib_converters()
start = pd.to_datetime("5-1-2012")
idx = pd.date_range(start, periods= 365)
df = pd.DataFrame({'Date': idx, 'A':np.random.random(365)})
fig, ax = plt.subplots()
ax.bar(df["Date"], df["A"], width=1)
plt.show()
Further options:
For other options see Pandas bar plot changes date format

How to format x axis time data from 'Month-Day Hour' (mm-dd HH) to just 'Hour' in matplotlib? Or how to place Hour data on the minor axis? [duplicate]

I have a series whose index is datetime that I wish to plot. I want to plot the values of the series on the y axis and the index of the series on the x axis. The Series looks as follows:
2014-01-01 7
2014-02-01 8
2014-03-01 9
2014-04-01 8
...
I generate a graph using plt.plot(series.index, series.values). But the graph looks like:
The problem is that I would like to have only year and month (yyyy-mm or 2016 March). However, the graph contains hours, minutes and seconds. How can I remove them so that I get my desired formatting?

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
# sample data
N = 30
drange = pd.date_range("2014-01", periods=N, freq="MS")
np.random.seed(365) # for a reproducible example of values
values = {'values':np.random.randint(1,20,size=N)}
df = pd.DataFrame(values, index=drange)
fig, ax = plt.subplots()
ax.plot(df.index, df.values)
ax.set_xticks(df.index)
# use formatters to specify major and minor ticks
ax.xaxis.set_major_formatter(mdates.DateFormatter("%Y-%m"))
ax.xaxis.set_minor_formatter(mdates.DateFormatter("%Y-%m"))
_ = plt.xticks(rotation=90)

You can try something like this:
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
df = pd.DataFrame({'values':np.random.randint(0,1000,36)},index=pd.date_range(start='2014-01-01',end='2016-12-31',freq='M'))
fig,ax1 = plt.subplots()
plt.plot(df.index,df.values)
monthyearFmt = mdates.DateFormatter('%Y %B')
ax1.xaxis.set_major_formatter(monthyearFmt)
_ = plt.xticks(rotation=90)

You should check out this native function of matplotlib:
fig.autofmt_xdate()
See examples on the source website Custom tick formatter

How to make bar plot with converting the month column in python?

I have a dataframe like this. The month column is in type of string.
I want to make a bar plot from 201501 to 201505 with x axis is month while y axis is total_gmv. x format is like Jan,2015 Feb 2015. So how can I realize it using python? Thanks.
month total_gmv
201501 NaN
201502 2.824294e+09
201503 7.742665e+09
201504 2.024132e+10
201505 6.705012e+10

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame(
{'month': ['201501', '201502', '201503', '201504', '201505'],
'total_gmv': [np.nan, 2.824294e+09, 7.742665e+09, 2.024132e+10, 6.705012e+10]})
df['month'] = pd.to_datetime(df['month'], format='%Y%m').dt.month
df = df.set_index('month')
print df
df.plot(kind='bar')
plt.show()
Result:
total_gmv
month
1 NaN
2 2.824294e+09
3 7.742665e+09
4 2.024132e+10
5 6.705012e+10

You should be able to force month to be a timestamp and then set it as an index and plot it.
df['month'] = pd.to_datetime(df.month)
ax = df.set_index('month').plot(kind='bar')
And you might have to change the date format.
import matplotlib.dates as mdates
ax.xaxis.set_major_formatter= mdates.DateFormatter('%b, %Y')
Check here for more

Previous replies have some clues but its does not show exhaustive answer.
You have to set custom xtick labels and rotate it like here:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(
{'month': ['201501', '201502', '201503', '201504', '201505'],
'total_gmv': [np.nan, 2.824294e+09, 7.742665e+09, 2.024132e+10, 6.705012e+10]})
df['month'] = pd.to_datetime(df['month'], format='%Y%m', errors='ignore')
ax = df.plot(kind='bar')
ax.set_xticklabels(df['month'].dt.strftime('%b, %Y'))
plt.xticks(rotation=0)
plt.show()

You should use matplotlib.pyplot and calendar module.
import matplotlib.pyplot as plt
import calendar
#change the numeric representation to texts (201501 -> Jan,2015)
df['month_name'] = [','.join([calendar.month_name[int(date[-1:-3]),date[-3:]] for date in df['month']
#change the type of df['month'] to int so plt can read it
df['month'].apply(int)
x = df['month']
y = df['total_gmv']
plt.bar(x, y, align = 'center')
#i'm not sure if you have to change the Series to a list; do whatever works
plt.xticks =(x, df['month_name'])
plt.show()