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Plotting more than 10K data point using Seaborn for x-axis as timestamp

I am trying to plot more than 10k data points, where I want to plot a data properties versus Timestamp. But on the x-axis the timestamps are overlapping and not visible.
How can I reduce the amount of labels on the x-axis, so that they are legible?
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
sns.set_style("whitegrid")
data = pd.read_csv('0912Testday4.csv',header=2)
for i in data.columns:
if i!='TIMESTAMP':
sns.lineplot(x="TIMESTAMP",y=i,data = data)
plt.title(f"{i} vs TIMESTAMP")
plt.show()
Example plot demonstrating the problem:
Update:TIMESTAMP was in string format by converting into datatime format it resolves the problem.
data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'])

Update:TIMESTAMP was in string format by converting into datetime format it resolves the problem.
data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'])

Please make sure that TIMESTAMP is a datetime object. This should not happen when the x axis is a datetime. (You can use pd.to_datetime to convert int, float, str, and ... to datetime.)
If TIMESTAMP is a datetime, you can use the autofmt_xdate() method:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
fig, ax = plt.subplots() # Create a figure and a set of subplots.
sns.set_style("whitegrid")
data = pd.read_csv('0912Testday4.csv',header=2)
# Use the following line if the TIMESTAMP is not a datetime.
# (You may need to change the format from "%Y-%m-%d %H:%M:%S+00:00".)
# data['TIMESTAMP'] = pd.to_datetime(data.TIMESTAMP, format="%Y-%m-%d %H:%M:%S+00:00")
for i in data.columns:
if i!='TIMESTAMP':
sns.lineplot(x="TIMESTAMP", y=i, data=data, ax=ax)
fig.autofmt_xdate() # rotate and right align date ticklabels
plt.title(f"{i} vs TIMESTAMP")
plt.show()

I didn't encounter such problem with sns.lineplot
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
# example data
time_stamps = pd.date_range('2019-01-01', '2020-01-01', freq='H')
vals =[np.random.randint(0, 1000) for i in time_stamps]
data_df = pd.DataFrame()
data_df['time'] = time_stamps
data_df['value'] = vals
print(data_df.shape)
# plotting
fig, ax = plt.subplots()
sns.lineplot(x='time', y='value', data=data_df)
plt.show()
sns automatically selects the x ticks and x labels.
alternatively, you can use ax.set_xticks and ax.set_xlabels to set the x ticks and x labels manually.
Also you may use fig.autofmt_xdate() to rotate the x labels

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

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()

Modify major and minor xticks for dates

I am plotting two pandas series. The index is a date (1-1 to 12-31)
s1.plot()
s2.plot()
pd.plot() interprets the dates and assigns them to axis values as such:
I would like to modify the major ticks to be the 1st of every month and minor ticks to be the days in between
This works:
%matplotlib notebook
import matplotlib as mpl
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('data.csv')
df['Date'] = pd.to_datetime(df['Date']).dt.strftime('%m-%d')
s2014max = df2014.groupby(['Date'], sort=True)['Data_Value'].max()/10
s2014min = df2014.groupby(['Date'], sort=True)['Data_Value'].min()/10
#remove the leap day and convert to datetime for plotting
s2014min = s2014min[s2014min.index != '02-29']
s2014max = s2014max[s2014max.index != '02-29']
dateslist = s2014min.index.tolist()
dates = [pd.datetime.strptime(date, '%m-%d').date() for date in dateslist]
plt.figure()
ax = plt.gca()
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.DayLocator())
monthFmt = mdates.DateFormatter('%b')
dayFmt = mdates.DateFormatter('%d')
ax.xaxis.set_major_formatter(monthFmt)
ax.xaxis.set_minor_formatter(dayFmt)
ax.tick_params(direction='out', pad=15)
s2014min.plot()
s2014max.plot()
This results in no ticks:

A possible way is to use matplotlib for plotting the dates instead of pandas.
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
dates = pd.date_range("2016-01-01", "2016-12-31" )
y = np.cumsum(np.random.normal(size=len(dates)))
df = pd.DataFrame({"Dates" : dates, "y": y})
fig, ax = plt.subplots()
ax.plot_date(df["Dates"], df.y, '-')
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.DayLocator())
monthFmt = mdates.DateFormatter('%b')
ax.xaxis.set_major_formatter(monthFmt)
plt.show()

You were so close! All you needed to do was add the formatters similar to how the other answer did it. Here is a working sample similar to your code (note I did mine in ipython notebook hence the %matplotlib inline).
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from datetime import datetime, timedelta
from random import random
y = [random() for i in range(25)]
x = [(datetime.now() - timedelta(days=i)) for i in range(25)]
x.reverse()
s = pd.Series(y, index=x) # NOTE: S, not df, since you said you were using series
# format the ticks
ax = plt.gca()
ax.xaxis.set_major_locator(mdates.MonthLocator())
ax.xaxis.set_minor_locator(mdates.DayLocator())
monthFmt = mdates.DateFormatter('%b')
dayFmt = mdates.DateFormatter('%d')
ax.xaxis.set_major_formatter(monthFmt) # This is what you needed
ax.xaxis.set_minor_formatter(dayFmt) # This is what you needed
ax.tick_params(direction='out', pad=15)
# format the coords message box
s.plot(figsize=(10,3))
which will look like this:

ploting subplot in matplotlib with pandas issue

i am try to plot subplot in matplotlib with pandas but there are issue i am facing. when i am plot subplot not show the date of stock...there is my program
import pandas as pd
import datetime
import matplotlib.pyplot as plt
import pandas.io.data
df = pd.io.data.get_data_yahoo('goog', start=datetime.datetime(2008,1,1),end=datetime.datetime(2014,10,23))
fig = plt.figure()
r = fig.patch
r.set_facecolor('#0070BB')
ax1 = fig.add_subplot(2,1,1, axisbg='#0070BB')
ax1.grid(True)
ax1.plot(df['Close'])
ax2 = fig.add_subplot(2,1,2, axisbg='#0070BB')
ax2.plot(df['Volume'])
plt.show()
run this program own your self and solve date issue.....

When you're calling matplotlib's plot(), you are only giving it one array (e.g. df['Close'] in the first case). When there's only one array, matplotlib doesn't know what to use for the x axis data, so it just uses the index of the array. This is why your x axis shows the numbers 0 to 160: there are presumably 160 items in your array.
Use ax1.plot(df.index, df['Close']) instead, since df.index should hold the date values in your pandas dataframe.
import pandas as pd
import datetime
import matplotlib.pyplot as plt
import pandas.io.data
df = pd.io.data.get_data_yahoo('goog', start=datetime.datetime(2008,1,1),end=datetime.datetime(2014,10,23))
fig = plt.figure()
r = fig.patch
r.set_facecolor('#0070BB')
ax1 = fig.add_subplot(2,1,1, axisbg='#0070BB')
ax1.grid(True)
ax1.plot(df.index, df['Close'])
ax2 = fig.add_subplot(2,1,2, axisbg='#0070BB')
ax2.plot(df.index, df['Volume'])
plt.show()