import pandas as pd
df = pd.read_excel(some data)
df2 = df.groupby(['Country', "Year"]).sum()
It looks like this:
Sales COGS Profit Month Number
Country Year
Canada 2013 3000
Canada 2014 3500
Other countries... other data
df3 = df2[[' Sales']]
I can plot it like this with the code:
df3.plot(kind="bar")
And it produces a chart
But I want to turn it into a line chart but my result from a simple plot is:
Stuck as to what one-liner will produce a chart that segments time on the x-axis but plots sales on y-axis with lines for different countries.
You have to stack Country column:
import matplotlib.pyplot as plt
df2 = df.groupby(['Country', 'Year'])['Sales'].sum().unstack('Country')
# Or df2.plot(title='Sales').set_xticks(df2.index)
ax = df2.plot(title='Sales')
ax.set_xticks(df2.index)
plt.show()
Output:
Related
I come from Javascript and struggle. Need to sort data by DatetimeIndex, further by the year.
CSV looks like this (i shortened it because of more than 1300 entries):
date,value
2016-05-09,1201
2017-05-10,2329
2018-05-11,1716
2019-05-12,10539
I wrote my code like this to throw away the first and last 2.5 percent of the dataframe:
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()
df = pd.read_csv( "fcc-forum-pageviews.csv", index_col="date", parse_dates=True).sort_values('value')
df = df.iloc[(int(round((df.count() / 100 * 2,5)[0]))):(int(round(((df.count() / 100 * 97,5)[0])-1)))]
df = df.sort_index()
Now I need to group my DatetimeIndex by years to plot it in a manner way by matplotlib. I struggle right here:
def draw_bar_plot():
df_bar = df
fig, ax = plt.subplots()
fig.figure.savefig('bar_plot.png')
return fig
I really dont know how to groupby years.
Doing something like:
print(df_bar.groupby(df_bar.index).first())
leads to:
value
date
2016-05-19 19736
2016-05-20 17491
2016-05-26 18060
2016-05-27 19997
2016-05-28 19044
... ...
2019-11-23 146658
2019-11-24 138875
2019-11-30 141161
2019-12-01 142918
2019-12-03 158549
How to group this by year? Maybe further explain how to get the data ploted by mathplotlib as a bar chart accurately.
This will group the data by year
df_year_wise_sum = df.groupby([df.index.year]).sum()
This line of code will give a bar plot
df_year_wise_sum.plot(kind='bar')
plt.savefig('bar_plot.png')
plt.show()
I am working on the Spotify dataset from Kaggle. I plotted a barplot showing the top artists with most songs in the dataframe.
But the X-axis is showing numbers and I want to show names of the Artists.
names = list(df1['artist'][0:19])
plt.figure(figsize=(8,4))
plt.xlabel("Artists")
sns.barplot(x=np.arange(1,20),
y=df1['song_title'][0:19]);
I tried both list and Series object type but both are giving error.
How to replace the numbers in xticks with names?
Imports
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
Data
Data from Spotify - All Time Top 2000s Mega Dataset
df = pd.read_csv('Spotify-2000.csv')
titles = pd.DataFrame(df.groupby(['Artist'])['Title'].count()).reset_index().sort_values(['Title'], ascending=False).reset_index(drop=True)
titles.rename(columns={'Title': 'Title Count'}, inplace=True)
# titles.head()
Artist Title Count
Queen 37
The Beatles 36
Coldplay 27
U2 26
The Rolling Stones 24
Plot
plt.figure(figsize=(8, 4))
chart = sns.barplot(x=titles.Artist[0:19], y=titles['Title Count'][0:19])
chart.set_xticklabels(chart.get_xticklabels(), rotation=90)
plt.show()
OK, so I didnt know this, although now it seems stupid not to do so in hindsight!
Pass names(or string labels) in the argument for X-axis.
use plt.xticks(rotate=90) so the labels don't overlap
I am using MatLibPlot to fetch data from an excel file and to create a scatter plot.
Here is a minimal sample table
In my scatter plot, I have two sets of XY values. In both sets, my X values are country population. I have Renewable Energy Consumed as my Y value in one set and Non-Renewable Energy Consumed in the other set.
For each Country, I would like to have a line from the renewable point to the non-renewable point.
My example code is as follows
import pandas as pd
import matplotlib.pyplot as plt
excel_file = 'example_graphs.xlsx'
datasheet = pd.read_excel(excel_file, sheet_name=0, index_col=0)
ax = datasheet.plot.scatter("Xcol","Y1col",c="b",label="set_one")
datasheet.scatter("Xcol","Y2col",c="r",label="set_two", ax=ax)
ax.show()
And it produces the following plot
I would love to be able to draw a line between the two sets of points, preferably a line I can change the thickness and color of.
As commented, you could simply loop over the dataframe and plot a line for each row.
import pandas as pd
import matplotlib.pyplot as plt
datasheet = pd.DataFrame({"Xcol" : [1,2,3],
"Y1col" : [25,50,75],
"Y2col" : [75,50,25]})
ax = datasheet.plot.scatter("Xcol","Y1col",c="b",label="set_one")
datasheet.plot.scatter("Xcol","Y2col",c="r",label="set_two", ax=ax)
for n,row in datasheet.iterrows():
ax.plot([row["Xcol"]]*2,row[["Y1col", "Y2col"]], color="limegreen", lw=3, zorder=0)
plt.show()
I am trying to generate a plot of the 6-month rolling Sharpe ratio using Python with Pandas/NumPy.
My input data is below:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
# Generate sample data
d = pd.date_range(start='1/1/2008', end='12/1/2015')
df = pd.DataFrame(d, columns=['Date'])
df['returns'] = np.random.rand(d.size, 1)
df = df.set_index('Date')
print(df.head(20))
returns
Date
2008-01-01 0.232794
2008-01-02 0.957157
2008-01-03 0.079939
2008-01-04 0.772999
2008-01-05 0.708377
2008-01-06 0.579662
2008-01-07 0.998632
2008-01-08 0.432605
2008-01-09 0.499041
2008-01-10 0.693420
2008-01-11 0.330222
2008-01-12 0.109280
2008-01-13 0.776309
2008-01-14 0.079325
2008-01-15 0.559206
2008-01-16 0.748133
2008-01-17 0.747319
2008-01-18 0.936322
2008-01-19 0.211246
2008-01-20 0.755340
What I want
The type of plot I am trying to produce is this or the first plot from here (see below).
My attempt
Here is the equation I am using:
def my_rolling_sharpe(y):
return np.sqrt(126) * (y.mean() / y.std()) # 21 days per month X 6 months = 126
# Calculate rolling Sharpe ratio
df['rs'] = calc_sharpe_ratio(df['returns'])
fig, ax = plt.subplots(figsize=(10, 3))
df['rs'].plot(style='-', lw=3, color='indianred', label='Sharpe')\
.axhline(y = 0, color = "black", lw = 3)
plt.ylabel('Sharpe ratio')
plt.legend(loc='best')
plt.title('Rolling Sharpe ratio (6-month)')
fig.tight_layout()
plt.show()
The problem is that I am getting a horizontal line since my function is giving a single value for the Sharpe ratio. This value is the same for all the Dates. In the example plots, they appear to be showing many ratios.
Question
Is it possible to plot a 6-month rolling Sharpe ratio that changes from one day to the next?
Approximately correct solution using df.rolling and a fixed window size of 180 days:
df['rs'] = df['returns'].rolling('180d').apply(my_rolling_sharpe)
This window isn't exactly 6 calendar months wide because rolling requires a fixed window size, so trying window='6MS' (6 Month Starts) throws a ValueError.
To calculate the Sharpe ratio for a window exactly 6 calendar months wide, I'll copy this super cool answer by SO user Mike:
df['rs2'] = [my_rolling_sharpe(df.loc[d - pd.offsets.DateOffset(months=6):d, 'returns'])
for d in df.index]
# Compare the two windows
df.plot(y=['rs', 'rs2'], linewidth=0.5)
I have prepared an alternative solution to your question, this one is based on using solely the window functions from pandas.
Here I have defined "on the fly" the calculation of the Sharpe Ratio, please consider for your solution the following parameters:
I have used a Risk Free rate of 2%
The dash line is just a Benchmark for the rolling Sharpe Ratio, the value is 1.6
So the code is the following
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style("whitegrid")
# Generate sample data
d = pd.date_range(start='1/1/2008', end='12/1/2015')
df = pd.DataFrame(d, columns=['Date'])
df['returns'] = np.random.rand(d.size, 1)
df = df.set_index('Date')
df['rolling_SR'] = df.returns.rolling(180).apply(lambda x: (x.mean() - 0.02) / x.std(), raw = True)
df.fillna(0, inplace = True)
df[df['rolling_SR'] > 0].rolling_SR.plot(style='-', lw=3, color='orange',
label='Sharpe', figsize = (10,7))\
.axhline(y = 1.6, color = "blue", lw = 3,
linestyle = '--')
plt.ylabel('Sharpe ratio')
plt.legend(loc='best')
plt.title('Rolling Sharpe ratio (6-month)')
plt.show()
print('---------------------------------------------------------------')
print('In case you want to check the result data\n')
print(df.tail()) # I use tail, beacause of the size of your window.
You should get something similar to this picture
I'm trying to plot timeseries revenue data by quarter with matplotlib.pyplot but keep getting an error. Below is my code and the errors The desired behavior is to plot the revenue data by quarter using matplotlib. When I try to do this, I get:
TypeError: Axis must havefreqset to convert to Periods
Is it because timeseries dates expressed as periods cannot be plotted in matplotlib? Below is my code.
def parser(x):
return pd.to_datetime(x, format='%m%Y')
tot = pd.read_table('C:/Desktop/data.txt', parse_dates=[2], index_col=[2], date_parser=parser)
tot = tot.dropna()
tot = tot.to_period('Q').reset_index().groupby(['origin', 'date'], as_index=False).agg(sum)
tot.head()
origin date rev
0 KY 2016Q2 1783.16
1 TN 2014Q1 32128.36
2 TN 2014Q2 16801.40
3 TN 2014Q3 33863.39
4 KY 2014Q4 103973.66
plt.plot(tot.date, tot.rev)
If you want to use matplotlib, the following code should give you the desired plot:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.DataFrame({'origin': ['KY','TN','TN','TN','KY'],
'date': ['2016Q2','2014Q1','2014Q2','2014Q3','2014Q4'],
'rev': [1783.16, 32128.36, 16801.40, 33863.39, 103973.66]})
x = np.arange(0,len(df),1)
fig, ax = plt.subplots(1,1)
ax.plot(x,df['rev'])
ax.set_xticks(x)
ax.set_xticklabels(df['date'])
plt.show()
You could use the xticks command and represent the data with a bar chart with the following code:
plt.bar(range(len(df.rev)), df.rev, align='center')
plt.xticks(range(len(df.rev)), df.date, size='small')
It seems like bug.
For me works DataFrame.plot:
ooc.plot(x='date', y='rev')