i'm a newbie in spyder especially matplotlib. Currently i am trying to display stock price data from dates of 2014-2019.
The unexpected errors i have encountered are the display of data is incorrect .
There are 2 flaws and workarounds i have attempted.
Is the error in displaying caused due to the first index being the year 2019 instead of the year 2014?
Flaw 1 : plt.yticks([0,140]) Does not even display ticks from 0 to 140.
Not displaying ticks from 0 to 140
Flaw 2 : If i remove the conversion of the Date to a datetime format. The display error will be half rectified but a new error will be displayed as the x axis cannot be displayed correctly.
Data is presented correctly from descending to ascending order but date cannot be displayed as it is not in datetime format.
In essence the main flaw is the tickers of the y axis cannot be displayed and the data needs to be presented in descending to ascending order.
I've spent hours trying to fix this, my hair is falling off, i'm gonna be bald soon.
The data is read from year 2019 to 2014.
As the data is organized as of column 1 is: 2019 and the last column is 2014.
I understand that the data is cramped during display but even changing the view to automatic and not inline will not solve this issue.
The code is as below
file = 'Microsoft.csv'
df = pd.read_csv(file)
## convert timestamp
# df['Date'] = pd.to_datetime(df['Date'], format = '%m/%d/%Y')
# df['Close'] = df['Close'].str.replace('$', '')
# df['Close'].astype('float')
##Not Necessary
# df['Close'].apply(lambda x: float(x))
# df.Close = float(df.Close)
# df['Close'] = df.Close.astype(float)
##Beginning of plot
plt.plot(df.Date, df.Close)
plt.yticks([0,140])
plt.ylim(0, 1511)
plt.suptitle('Stock Price')
plt.title('Microsoft', fontdict={'fontsize':15,'fontweight':'bold'})
plt.xlabel('Year')
plt.ylabel('Price in USD')
plt.show()
This is the original plotting without adding the yticks.
X-axis not converted to datetime, Y-Axis does not have yticks
Any help would be grateful, the goal is to display the plot from descending to ascending order with yticks being able to display from any order 0,20,40,60,80,100,120,140 or any amount.
Related
I'm currently building a model to predict daily stock price based on daily data for thousands of stocks. In the data, I've got the daily data for all stocks, however they are for different lengths. Eg: for some stocks I have daily data from 2000 to 2022, and for others I have data from 2010 to 2022.
Many dates are also obviously repeated for all stocks.
While I was learning autogluon, I used the following function to format timeseries data so it can work with .fit():
def forward_fill_missing(ts_dataframe: TimeSeriesDataFrame, freq="D") -> TimeSeriesDataFrame:
original_index = ts_dataframe.index.get_level_values("timestamp")
start = original_index[0]
end = original_index[-1]
filled_index = pd.date_range(start=start, end=end, freq=freq, name="timestamp")
return ts_dataframe.droplevel("item_id").reindex(filled_index, method="ffill")
ts_dataframe = ts_dataframe.groupby("item_id").apply(forward_fill_missing)
This worked, however I was trying this for data for just one item_id and now I have thousands.
When I use this now, I get the following error: ValueError: cannot reindex from a duplicate axis
It's important to note that I have already foreward filled my data with pandas, and the ts_dataframe shouldn't have any missing dates or values, but when I try to use it with .fit() I get the following error:
ValueError: Frequency not provided and cannot be inferred. This is often due to the time index of the data being irregularly sampled. Please ensure that the data set used has a uniform time index, or create the TimeSeriesPredictorsettingignore_time_index=True.
I assume that this is because I have only filled in missing data and dates, but not taken into account the varying number of days available for every stock individually.
For reference, here's how I have formatted the data with pandas:
df = pd.read_csv(
"/content/drive/MyDrive/stock_data/training_data.csv",
parse_dates=["Date"],
)
df["Date"] = pd.to_datetime(df["Date"], errors="coerce", dayfirst=True)
df.fillna(method='ffill', inplace=True)
df = df.drop("Unnamed: 0", axis=1)
df[:11]
How can I format the data so I can use it with .fit()?
Thanks!
I am working on a dataset which shows the budget spent on movies. I want make a plot which contains the top 15 highest budget movies.
#sort the 'budget' column in decending order and store it in the new dataframe.
info = pd.DataFrame(dp['budget'].sort_values(ascending = False))
info['original_title'] = dp['original_title']
data = list(map(str,(info['original_title'])))
#extract the top 10 budget movies data from the list and dataframe.
x = list(data[:10])
y = list(info['budget'][:10])
This was the ouput i got
C:\Users\Phillip\AppData\Local\Temp\ipykernel_7692\1681814737.py:2: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.
y = list(info['budget'][:5])
I'm new to the data analysis scene so i'm confused on how else to go about the problem
A simple example using a movie dataset I found online:
import pandas as pd
url = "https://raw.githubusercontent.com/erajabi/Python_examples/master/movie_sample_dataset.csv"
df = pd.read_csv(url)
# Bar plot of 15 highest budgets:
df.nlargest(n=15, columns="budget").plot.bar(x="movie_title", y="budget")
You can customize your plot in various ways by adding arguments to the .bar(...) call.
I have a dataframe region_cumulative_df_sel as below:
Month-Day regions RAIN_PERCENTILE_25 RAIN_PERCENTILE_50 RAIN_PERCENTILE_75 RAIN_MEAN RAIN_MEDIAN
07-01 1 0.0611691028 0.2811064720 1.9487996101 1.4330813885 0.2873695195
07-02 1 0.0945720226 0.8130480051 4.5959815979 2.9420840740 1.0614821911
07-03 1 0.2845511734 1.1912839413 5.5803232193 3.7756001949 1.1988518238
07-04 1 0.3402922750 3.2274529934 7.4262523651 5.2195668221 3.2781836987
07-05 1 0.4680584669 5.2418060303 8.6639881134 6.9092760086 5.3968687057
07-06 1 2.4329853058 7.3453550339 10.8091869354 8.7898645401 7.5020875931
... ...
... ...
... ...
06-27 1 382.7809448242 440.1162109375 512.6233520508 466.4956665039 445.0971069336
06-28 1 383.8329162598 446.2222900391 513.2116699219 467.9851379395 451.1973266602
06-29 1 385.7786254883 449.5384826660 513.4027099609 469.5671691895 451.2281188965
06-30 1 386.7952270508 450.6524658203 514.0201416016 471.2863159180 451.2484741211
The index "Month-Day" is a type of String indicating the first day and the last day of a calendar year instead of type of datetime.
I need to use hvplot to develop an interactive plot.
region_cumulative_df_sel.hvplot(width=900)
It is hard to view the labels on the x axis. How can change the xticks to show only 1st of each month, e.g. "07-01", "08-01", "09-01", ... ..., "06-01"?
I tried #Redox code as below:
region_cumulative_df_sel['Month-Day'] = pd.to_datetime(region_cumulative_df_sel['Month-Day'],format="%m-%d") ##Convert to datetime
from bokeh.models.formatters import DatetimeTickFormatter
## Set format for showing x-axis ... you only need days, but in case counts change
formatter = DatetimeTickFormatter(days=["%m-%d"], months=["%m-%d"], years=["%m-%d"])
region_cumulative_df_sel.plot(x='Month-Day', xformatter=formatter, y=['RAIN_PERCENTILE_25','RAIN_PERCENTILE_50','RAIN_PERCENTILE_75','RAIN_MEAN','RAIN_MEDIAN'], width=900, ylabel="Rainfall (mm)",
rot=90, title="Cumulative Rainfall")
This is what I have generated.
How can I shift the xticks on the x-axis to align with the Month-Day values. Also the popup window shows "1900" as year for Month-Day column. Can the year segment be removed?
The x-axis data is in string format. So, holoviews thinks this is categorical and plotting every row. You need to convert it to datetime and this will allow the plotting to be in the format you need. I am taking a simple example and showing how to do this... should work in your case as well...
##My month-day column is string - 07-01 07-02 07-03 07-04 ... 12-31
df['Month-Day']=pd.to_datetime(df['Month-Day'],format="%m-%d") ##Convert to datetime
df['myY']=np.random.randint(100, size=(len(df))) ##Random Y data
from bokeh.models.formatters import DatetimeTickFormatter
## Set format for showing x-axis ... you only need days, but in case counts change
formatter = DatetimeTickFormatter(days=["%m-%d"], months=["%m-%d"], years=["%m-%d"])
##Plot graph
df.plot(x='Month-Day',xformatter=formatter)#.opts(xticks=4, xrotation=90)
#Redox is on the right track here. The issue is with the way the Month-Day column is converted to a datetime; pandas is assuming the year is 1900 for every row.
Essentially you need to attach a year to the Month-Day in some way.
See the example below, this takes the first month-day string, prepends "2022-" and generates sequential daily values for every row (but there are a few ways of doing this).
code:
import pandas as pd
import numpy as np
import hvplot.pandas
from bokeh.models.formatters import DatetimeTickFormatter
dates = pd.date_range("2021-07-01", "2022-06-30", freq="D")
df = pd.DataFrame({
"md": dates.strftime("%m-%d"),
"ign": np.cumsum(np.random.normal(10, 5, len(dates))),
"sup": np.cumsum(np.random.normal(20, 10, len(dates))),
"imp": np.cumsum(np.random.normal(30, 15, len(dates))),
})
df["time"] = pd.date_range("2021-" + df.md[0], periods=len(df.index), freq="D")
formatter = DatetimeTickFormatter(
days=["%m-%d"], months=["%m-%d"], years=["%m-%d"])
df.hvplot(x='time', xformatter=formatter, y=['ign', 'sup', 'imp'],
width=900, ylabel="Index", rot=90, title="Cumulative ISI")
I have a data frame with perfectly organised timestamps, like below:
It's a web log, and the timestamps go though the whole year. I want to cut them into each day and show the visits within each hour and plot them into the same figure and stack them all together. Just like the pic shown below:
I am doing well on cutting them into days and plot the visits of a day individually, but I am having trouble plotting them and stacking them together. The primary tool I am using is Pandas and Matplotlib.
Any advices and suggestions? Much Appreciated!
Edited:
My Code is as below:
The timestamps are: https://gist.github.com/adamleo/04e4147cc6614820466f7bc05e088ac5
And the dataframe looks like this:
I plotted the timestamp density through the whole period used the code below:
timestamps_series_all = pd.DatetimeIndex(pd.Series(unique_visitors_df.time_stamp))
timestamps_series_all_toBePlotted = pd.Series(1, index=timestamps_series_all)
timestamps_series_all_toBePlotted.resample('D').sum().plot()
and got the result:
I plotted timestamps within one day using the code:
timestamps_series_oneDay = pd.DatetimeIndex(pd.Series(unique_visitors_df.time_stamp.loc[unique_visitors_df["date"] == "2014-08-01"]))
timestamps_series_oneDay_toBePlotted = pd.Series(1, index=timestamps_series_oneDay)
timestamps_series_oneDay_toBePlotted.resample('H').sum().plot()
and the result:
And now I am stuck.
I'd really appreciate all of your help!
I think you need pivot:
#https://gist.github.com/adamleo/04e4147cc6614820466f7bc05e088ac5 to L
df = pd.DataFrame({'date':L})
print (df.head())
date
0 2014-08-01 00:05:46
1 2014-08-01 00:14:47
2 2014-08-01 00:16:05
3 2014-08-01 00:20:46
4 2014-08-01 00:23:22
#convert to datetime if necessary
df['date'] = pd.to_datetime(df['date'] )
#resample by Hours, get count and create df
df = df.resample('H', on='date').size().to_frame('count')
#extract date and hour
df['days'] = df.index.date
df['hours'] = df.index.hour
#pivot and plot
#maybe check parameter kind='density' from http://stackoverflow.com/a/33474410/2901002
#df.pivot(index='days', columns='hours', values='count').plot(rot='90')
#edit: last line change to below:
df.pivot(index='hours', columns='days', values='count').plot(rot='90')
I am attempting to plot a candlestick chart using matplotlib, with hourly candles. However my output looks strange and it seems to be plotting multiple "hours" on one candle.
My code is as follows:
cursor = conx.cursor()
query= 'SELECT ticker,date,time,open,low,high,close FROM eurusd WHERE date > "2014-01-28"'
cursor.execute(query)
for line in cursor:
#appendLine in correct format for candlesticks - date,open,close,high,low
date=date2num(line[1])
open=(line[3])
high=(line[5])
low=(line[4])
close=(line[6])
appendLine = date,open,close,high,low
candleAr.append(appendLine)
fig = plt.figure()
ax1 = plt.subplot(1,1,1)
candlestick(ax1, candleAr, width=0.6, colorup='g', colordown='r')
ax1.grid(True)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
And my output looks like the following:
Do I have to manipulate the "date2num" function to account for the fact that my data is hourly and not daily?
Managed to answer my own question - It was due to the date2num output having repeated values and was cramming all the days hourly bars into one. I had to add my date and time together to get a datetime, and then use the date2num on the date time (rather than date)
date=[]
open=[]
low=[]
high=[]
close=[]
candleAr=[]
for line in cursor:
time1=datetime.time(0,0)
time=datetime.datetime.combine(line[1],time1)
time=time+line[2]
appendLine = date2num(time),line[3],line[6],line[5],line[4]