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How do I clean up the x axis?

I have tried plt.gcf().autofmt_xdate() but that doesn't fix the overlapping dates on the x axis. How do I clean the x axis to every week instead of everyday?
# Convert string column into date
df['date'] = pd.to_datetime(df['date'], format = "%Y-%m-%d")
plt.figure(figsize=(14,10))
plt.title("Daily Tests")
plt.ylabel("Number of confirmed cases")
plt.xlabel("Date")
sns.barplot(x=df['date'], y=df['confirmed'])
plt.show()
The graph:
any suggestions would be appreciated.

Set the interval of mdates.DayLocator(interval=14) in order to control the time series data of x-axis. In this case, it is set to two weeks.
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
fig = plt.figure(figsize=(14,10))
ax = fig.add_subplot(111)
sns.barplot(x=df['date'], y=df['confirmed'], ax=ax)
ax.set_title("Daily Tests")
ax.set_ylabel("Number of confirmed cases")
ax.set_xlabel("Date")
days = mdates.DayLocator(interval=14)
days_fmt = mdates.DateFormatter('%m-%d')
ax.xaxis.set_major_locator(days)
ax.xaxis.set_major_formatter(days_fmt)
plt.show()

How to set x-ticks to months with `set_major_locator`?

I am trying to use the following code to set the x-ticks to [Jan., Feb., ...]
import matplotlib.pyplot as plt
from matplotlib.dates import MonthLocator, DateFormatter
fig = plt.figure(figsize=[10, 5])
ax = fig.add_subplot(111)
ax.plot(np.arange(1000))
ax.xaxis.set_major_locator(MonthLocator())
ax.xaxis.set_major_formatter(DateFormatter('%b'))
I get the following figure, without x-ticks
I'm wondering why all x-ticks disappeared? I wrote the above code with reference to this implementation
Many thanks.

It is not very clear the type of data you currently have. But below are my suggestions for plotting the month on the x-axis:
Transform your date using pd.to_datetime
Set it to your dataframe index.
Call explicitly the plt.set_xticks() method
Below one example with re-created data:
from datetime import datetime as dt
from datetime import timedelta
### create sample data
your_df = pd.DataFrame()
your_df['vals'] = np.arange(1000)
## make sure your datetime is considered as such by pandas
your_df['date'] = pd.to_datetime([dt.today()+timedelta(days=x) for x in range(1000)])
your_df= your_df.set_index('date') ## set it as index
### plot it
fig = plt.figure(figsize=[10, 5])
ax = fig.add_subplot(111)
ax.plot(your_df['vals'])
plt.xticks(rotation='vertical')
ax.xaxis.set_major_locator(MonthLocator())
ax.xaxis.set_major_formatter(DateFormatter('%b'))
Note that if you do not want every month plotted, you can let matplotlib handle that for you, by removing the major locator.
fig = plt.figure(figsize=[10, 5])
ax = fig.add_subplot(111)
ax.plot(your_df['vals'])
plt.xticks(rotation='vertical')
# ax.xaxis.set_major_locator(MonthLocator())
ax.xaxis.set_major_formatter(DateFormatter('%b'))
Added Went into the link provided, and you do have a DATE field in the dataset used (boulder-precip.csv). You can actually follow the same procedure and have it plotted on a monthly-basis:
df = pd.read_csv('boulder-precip.csv')
df['DATE'] = pd.to_datetime(df['DATE'])
df = df.set_index('DATE')
fig = plt.figure(figsize=[10, 5])
ax = fig.add_subplot(111)
ax.plot(df['PRECIP'])
plt.xticks(rotation='vertical')
ax.xaxis.set_major_locator(MonthLocator())
ax.xaxis.set_major_formatter(DateFormatter('%b'))

Set x axis labels for joyplot

I have written the code below to visualise a joyplot. When trying to change the x axis labels using axes.set_xticks, I get the error: AttributeError: 'list' object has no attribute 'set_xticks'
import joypy
import pandas as pd
from matplotlib import pyplot as plt
data = pd.DataFrame.from_records([['twitter', 1],
['twitter', 6],
['wikipedia', 1],
['wikipedia', 3],
['indymedia', 1],
['indymedia', 9]], columns=['platform','day'])
# Get number of days in the dataset
numdays = max(set(data['day'].tolist()))
# Generate date strings from a manually set start date
start_date = "2010-01-01"
dates = pd.date_range(start_date, periods=numdays)
dates = [str(date)[:-9] for date in dates]
fig, axes = joypy.joyplot(data,by="platform")
axes.set_xticks(range(numdays)); axes.set_xticklabels(dates)
plt.show()
The expected output should look something like the following but with the dates from dates as ticklabels.

Since joypy.joyplot(..) returns a tuple of figure, axes and axes should be list of axes, you probably want to set the labels for the last axes,
axes[-1].set_xticks(range(numdays))
axes[-1].set_xticklabels(dates)

To make date plots with python matplotlib do you should use plot_date function.
fig, ax = plt.subplots()
ax.plot_date(dates, data1, '-')
I put the complete example in pastebin, follow the link:
https://pastebin.com/sVPUZaeM
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
from random import randrange, random
from datetime import datetime
#generate date list
start_date = np.datetime64('2010-01-01').astype(datetime)
numdays = 10
dates = pd.date_range(start_date, periods=numdays)
#Generate data example
data1 = [(random()+idx)**1.2 for idx in range(len(dates))]
data2 = [(random()+idx)**1.5 for idx in range(len(dates))]
#plot
fig, ax = plt.subplots()
ax.plot_date(dates, data1, '-')
ax.plot_date(dates, data2, '-')
#set the label for x and y and title
plt.title('Matplot lib dates wc example')
plt.xlabel('Dates')
plt.ylabel('Random values example')
#date format
ax.fmt_xdata = DateFormatter('%Y%m%d')
ax.grid(True)
fig.autofmt_xdate()
plt.show()
Python version tested successfully: 2.7.12
This code generates: this follow plot

Plotting candlestick with matplotlib for time series w/o weekend gaps

trying to plot a candlestick serie after importing datas from yahoo-finance. I'm using python 2.7
I have already a serie plotted and I want to add the same one as candlestick but I don't see how I can do that :
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick2_ohlc
#Reset the index to remove Date column from index
df_ohlc = data.reset_index()
#Naming columns
df_ohlc.columns = ["Date","Open","High",'Low',"Close", "Adj Close", "Volume"]
#Normal plot
ax1 = plt.subplot()
ax1.plot(df_ohlc["Date"], df_ohlc["Close"], label = "Price", color="blue", linewidth=2.0)
#Candle plot
candlestick2_ohlc(ax1,df_ohlc['Open'],df_ohlc['High'],df_ohlc['Low'],df_ohlc['Close'],width=0.6)
If I plot candlestick alone, it looks fine but the x axis is a list of integers.
If I plot candlestick alone after converting df_ohlc["Date"] to float then reconverting to datetime, it plots the serie with the correct x axis but there are gaps on the weekend even if the serie isn't defined for these dates.
Is there a way to plot both series at the same time ? I'm planning to add more series like moving average, OLS, Bollinger etc...

You can remove weekend gaps and make human-readable dates xticklabels in this way. Note that, this script is written in python 3 and there may be some differences from python 2.
import quandl
import numpy as np
from mpl_finance import candlestick_ohlc
import matplotlib.pyplot as plt
# getting data and modifying it to remove gaps at weekends
r = quandl.get('WIKI/AAPL', start_date='2016-01-01', end_date='2017-11-10')
date_list = np.array(r.index.to_pydatetime())
plot_array = np.zeros([len(r), 5])
plot_array[:, 0] = np.arange(plot_array.shape[0])
plot_array[:, 1:] = r.iloc[:, :4]
# plotting candlestick chart
fig, ax = plt.subplots()
num_of_bars = 100 # the number of candlesticks to be plotted
candlestick_ohlc(ax, plot_array[-num_of_bars:], colorup='g', colordown='r')
ax.margins(x=0.0, y=0.1)
ax.yaxis.tick_right()
x_tick_labels = []
ax.set_xlim(right=plot_array[-1, 0]+10)
ax.grid(True, color='k', ls='--', alpha=0.2)
# setting xticklabels actual dates instead of numbers
indices = np.linspace(plot_array[-num_of_bars, 0], plot_array[-1, 0], 8, dtype=int)
for i in indices:
date_dt = date_list[i]
date_str = date_dt.strftime('%b-%d')
x_tick_labels.append(date_str)
ax.set(xticks=indices, xticklabels=x_tick_labels)
plt.show()

I really need more information about your code and your dataframe, but you can use this example to do a candlestick
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
from matplotlib.finance import candlestick_ohlc
import matplotlib.dates as mdates
import datetime as dt
#Reset the index to remove Date column from index
df_ohlc = df.reset_index()
#Naming columns
df_ohlc.columns = ["Date","Open","High",'Low',"Close", "Adj Close", "Volume"]
#Converting dates column to float values
df_ohlc['Date'] = df_ohlc['Date'].map(mdates.date2num)
#Making plot
fig = plt.figure()
fig.autofmt_xdate()
ax1 = plt.subplot2grid((6,1), (0,0), rowspan=6, colspan=1)
#Converts raw mdate numbers to dates
ax1.xaxis_date()
plt.xlabel("Date")
print(df_ohlc)
#Making candlestick plot
candlestick_ohlc(ax1,df_ohlc.values,width=1, colorup='g', colordown='k',alpha=0.75)
plt.ylabel("Price")
plt.legend()
plt.show()

Change date labels when plotting pandas series with matplotlib

I have a time series of monthly data like this, and plot it like this:
rng = pd.date_range('1965-01-01', periods=600, freq='M')
ts = pd.Series(np.random.randn(len(rng)), index=rng)
fig, ax = plt.subplots()
ts.plot(ax=ax)
The major tick marks are set every 10 years, beginning in 1969. I'd like to change this so they start in 1975. After looking at some matplotlib samples (here and here) I tried adding
from matplotlib.dates import YearLocator, DateFormatter
decs = YearLocator(10) # decades
decsFmt = DateFormatter("%Y")
ax.xaxis.set_major_locator(decs)
ax.xaxis.set_major_formatter(decsFmt)
datemin = pd.datetime(ts.index.min().year, 1, 1)
datemax = pd.date(ts.index.max().year + 1, 1, 1)
ax.set_xlim(datemin, datemax)
but this doesn't work.

If you want to use matplotlib to set axis limits you will need to turn off pandas' date formatting.
Just change the line to
ts.plot(x_compat=True, ax=ax)
and it should work.