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.
Related
I'm trying to plot the stock price and the earnings on the graph but for some reason I'm getting this:
Graph1
Please see my code below:
import matplotlib.pyplot as plt
import yfinance as yf
import pandas
import pandas_datareader
import matplotlib
t = yf.Ticker("T")
df1 = t.earnings
df1['Earnings'].plot(label = 'earnings', figsize = (15,7), color='green')
print(df1)
df2 = t.history(start = '2018-01-01', end = '2021-01-01', actions = False, rounding = True)
df2['Close'].plot(label = 'price', figsize = (15,7),color = 'blue')
plt.show()
Could someone help me?
Thanks in advance.
Plotting in pandas is easy to create graphs, but if you try to overlay them with time series data, as in this example, you will encounter problems. There are many approaches, but the method that I find easiest is to convert the data level to the gregorian calendar managed by matplotlib and create the graph. Finally, you can either convert it to your preferred formatting, etc., or use the automatic formatter and locator.
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import yfinance as yf
import pandas as pd
t = yf.Ticker("T")
df1 = t.earnings
df1.index = pd.to_datetime(df1.index, format='%Y')
df1.index = mdates.date2num(df1.index)
ax = df1['Earnings'].plot(label='earnings', figsize=(15, 7), color='green')
df2 = t.history(start='2018-01-01', end='2021-01-01', actions=False, rounding=True)
df2.index = mdates.date2num(df2.index)
df2['Close'].plot(label='price', ax=ax,color='blue', secondary_y=True)
#ax.set_xticklabels([x.strftime('%Y-%m') for x in mdates.num2date(df2.index)][::125])
locator = mdates.AutoDateLocator()
formatter = mdates.ConciseDateFormatter(locator)
ax.xaxis.set_major_locator(locator)
ax.xaxis.set_major_formatter(formatter)
plt.show()
I have a dataframe:
df = pd.DataFrame({"max_cr_date":{"0":1569115380000,"1":1569115500000,"2":1569115560000,"3":1569115620000,"4":1569115680000,"5":1569115740000,"6":1569115800000,"7":1569115860000,"8":1569115920000,"9":1569115980000,"10":1569116040000,"11":1569116100000,"12":1569116160000,"13":1569116220000,"14":1569130800000,"15":1569130800000,"16":1569130800000,"17":1569130800000,"18":1569130860000,"19":1569130860000,"20":1569130860000,"21":1569130860000,"22":1569131100000,"23":1569131100000,"24":1569131160000,"25":1569131160000,"26":1569131220000,"27":1569131220000,"28":1569131280000,"29":1569131280000,"30":1569131340000,"31":1569131340000,"32":1569131400000,"33":1569131400000,"34":1569131460000,"35":1569131460000,"36":1569131520000,"37":1569131520000,"38":1569131580000,"39":1569131580000,"40":1569131640000,"41":1569131640000,"42":1569131700000,"43":1569131700000},"cnt":{"0":14,"1":14,"2":14,"3":14,"4":14,"5":14,"6":14,"7":14,"8":14,"9":14,"10":14,"11":14,"12":14,"13":14,"14":11,"15":12,"16":13,"17":14,"18":11,"19":12,"20":13,"21":14,"22":11,"23":12,"24":11,"25":12,"26":11,"27":12,"28":11,"29":12,"30":11,"31":12,"32":11,"33":12,"34":11,"35":12,"36":11,"37":12,"38":11,"39":12,"40":11,"41":12,"42":11,"43":12},"uuid":{"0":80,"1":66,"2":70,"3":80,"4":72,"5":110,"6":358,"7":123,"8":110,"9":123,"10":96,"11":89,"12":83,"13":58,"14":7,"15":28,"16":9,"17":5,"18":129,"19":116,"20":266,"21":87,"22":57,"23":86,"24":99,"25":36,"26":89,"27":30,"28":88,"29":18,"30":75,"31":26,"32":94,"33":29,"34":81,"35":32,"36":64,"37":19,"38":74,"39":26,"40":77,"41":17,"42":51,"43":21}})
df.max_cr_date = pd.to_datetime(df.max_cr_date, unit='ms')
df
df.max_cr_date.agg(['min', 'max'])
min 2019-09-22 01:23:00
max 2019-09-22 05:55:00
Name: max_cr_date, dtype: datetime64[ns]
When I try to plot the dataframe using seaborn, I get wrong xlim. For example, max_cr_date range is from 2019-09-22 01:23:00 to 2019-09-22 05:55:00, but on graph you can see year 2000, 2004...
How to set xlim to min/max of the max_cr_date column?
Regards.
You can do in this way:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib.dates as mdates
df.max_cr_date = pd.to_datetime(df.max_cr_date, unit='ms')
ax = sns.scatterplot(data=df, x="max_cr_date", y="uuid", hue='cnt', palette="vlag")
ax.set_xlim(df['max_cr_date'].min(), df['max_cr_date'].max())
myFmt = mdates.DateFormatter('%H:%M')
ax.xaxis.set_major_formatter(myFmt)
for item in ax.get_xticklabels():
item.set_rotation(45)
plt.show()
I am currently working on visualizing datasets with Seaborn and Pandas. I have some time-dependent data that I would like to graph in bar charts.
However, I am battling with two issues in Seaborn:
Formatting dates on the x-axis
Only showing a handful of dates (as
it doesn't make sense to have every day labeled on a 6 month graph)
I have found a solution for my issues in normal Matplotlib, which is:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
N = 20
np.random.seed(2022)
dates = pd.date_range('1/1/2014', periods=N, freq='m')
df = pd.DataFrame(
data={'dt':dates, 'val': np.random.randn(N)}
)
fig, ax = plt.subplots(figsize=(10, 6))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
ax.bar(df['dt'], df['val'], width=25, align='center')
However, I already have most of my graphs done in Seaborn, and I would like to stay consistent. Once I convert the previous code into Seaborn, I lose the ability to format the dates:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import numpy as np
N = 20
np.random.seed(2022)
dates = pd.date_range('1/1/2014', periods=N, freq='m')
df = pd.DataFrame(
data={'dt':dates, 'val': np.random.randn(N)}
)
fig, ax = plt.subplots(1,1)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%y-%m'))
sns.barplot(x='dt', y='val', data=df)
fig.autofmt_xdate()
When I run the code, the date format remains unchanged and I can't locate any dates with DateLocator.
Is there any way for me to format my X-Axis for dates in Seaborn in a way similar to Matplotlib with DateLocator and DateFormatter?
No, you cannot use seaborn.barplot in conjunction with matplotlib.dates ticking. The reason is that the ticks for seaborn barplots are at integer positions (0,1,..., N-1). So they cannot be interpreted as dates.
You have three options:
Use seaborn, and loop through the labels and set them to anything you want
Not use seaborn and have the advantages (and disadvantages) of matplotlib.dates tickers available.
Change the format in the dataframe prior to plotting.
Tested in python 3.10, pandas 1.5.0, matplotlib 3.5.2, seaborn 0.12.0
N = 20
np.random.seed(2022)
dates = pd.date_range('1/1/2014', periods=N, freq='m')
df = pd.DataFrame(data={'dates': dates, 'val': np.random.randn(N)})
# change the datetime format in the dataframe prior to plotting
df.dates = df.dates.dt.strftime('%Y-%m')
fig, ax = plt.subplots(1,1)
sns.barplot(x='dates', y='val', data=df)
xticks = ax.get_xticks()
xticklabels = [x.get_text() for x in ax.get_xticklabels()]
_ = ax.set_xticks(xticks, xticklabels, rotation=90)
N = 20
np.random.seed(2022)
dates = pd.date_range('1/1/2014', periods=N, freq='m')
df = pd.DataFrame(data={'dates': dates, 'val': np.random.randn(N)})
df.dates = df.dates.dt.strftime('%Y-%m')
fig, ax = plt.subplots(figsize=(10, 6))
sns.barplot(x='dates', y='val', data=df)
xticks = ax.get_xticks()
xticklabels = [x.get_text() if not i%2 == 0 else '' for i, x in enumerate(ax.get_xticklabels())]
_ = ax.set_xticks(xticks, xticklabels)
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
This code gives plot of candlesticks with moving averages but the x-axis is in index, I need the x-axis in dates.
What changes are required?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_finance import candlestick2_ohlc
#date format in data-> dd-mm-yyyy
nif = pd.read_csv('data.csv')
#nif['Date'] = pd.to_datetime(nif['Date'], format='%d-%m-%Y', utc=True)
mavg = nif['Close'].ewm(span=50).mean()
mavg1 = nif['Close'].ewm(span=13).mean()
fg, ax1 = plt.subplots()
cl = candlestick2_ohlc(ax=ax1,opens=nif['Open'],highs=nif['High'],lows=nif['Low'],closes=nif['Close'],width=0.4, colorup='#77d879', colordown='#db3f3f')
mavg.plot(ax=ax1,label='50_ema')
mavg1.plot(color='k',ax=ax1, label='13_ema')
plt.legend(loc=4)
plt.subplots_adjust(left=0.09, bottom=0.20, right=0.94, top=0.90, wspace=0.2, hspace=0)
plt.show()
Output:
I also had a lot of "fun" with this in the past... Here is one way of doing it using mdates:
import pandas as pd
import pandas_datareader.data as web
import datetime as dt
import matplotlib.pyplot as plt
from matplotlib.finance import candlestick_ohlc
import matplotlib.dates as mdates
ticker = 'MCD'
start = dt.date(2014, 1, 1)
#Gathering the data
data = web.DataReader(ticker, 'yahoo', start)
#Calc moving average
data['MA10'] = data['Adj Close'].rolling(window=10).mean()
data['MA60'] = data['Adj Close'].rolling(window=60).mean()
data.reset_index(inplace=True)
data['Date']=mdates.date2num(data['Date'].astype(dt.date))
#Plot candlestick chart
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax2 = fig.add_subplot(111)
ax3 = fig.add_subplot(111)
ax1.xaxis_date()
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m-%Y'))
ax2.plot(data.Date, data['MA10'], label='MA_10')
ax3.plot(data.Date, data['MA60'], label='MA_60')
plt.ylabel("Price")
plt.title(ticker)
ax1.grid(True)
plt.legend(loc='best')
plt.xticks(rotation=45)
candlestick_ohlc(ax1, data.values, width=0.6, colorup='g', colordown='r')
plt.show()
Output:
Hope this helps.
Simple df:
Using plotly:
import plotly.figure_factory
fig = plotly.figure_factory.create_candlestick(df.open, df.high, df.low, df.close, dates=df.ts)
fig.show()
will automatically parse the ts column to be displayed correctly on x.
Clunky workaround here, derived from other post (if i can find again, will reference). Using a pandas df, plot by index and then reference xaxis tick labels to date strings for display. Am new to python / matplotlib, and this this solution is not so flexible, but it works basically. Also using a pd index for plotting removes the blank 'weekend' daily spaces on market price data.
Matplotlib xaxis index as dates
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_finance import candlestick2_ohlc
from mpl_finance import candlestick_ohlc
%matplotlib notebook # for Jupyter
# Format m/d/Y,Open,High,Low,Close,Adj Close,Volume
# csv data does not include NaN, or 'weekend' lines,
# only dates from which prices are recorded
DJIA = pd.read_csv('yourFILE.csv') #Format m/d/Y,Open,High,
Low,Close,Adj Close,Volume
print(DJIA.head())
fg, ax1 = plt.subplots()
cl =candlestick2_ohlc(ax=ax1,opens=DJIA['Open'],
highs=DJIA['High'],lows=DJIA['Low'],
closes=DJIA['Close'],width=0.4, colorup='#77d879',
colordown='#db3f3f')
ax1.set_xticks(np.arange(len(DJIA)))
ax1.set_xticklabels(DJIA['Date'], fontsize=6, rotation=-90)
plt.show()