The dataframe I created is as follows:
import pandas as pd
import numpy as np
import seaborn as sns
date = pd.date_range('2003-01-01', '2022-11-01', freq='MS').strftime('%Y-%m-%d').tolist()
mom = [np.nan] + list(np.repeat([0.01], 238))
cpi = [100] + list(np.repeat([np.nan], 238))
df = pd.DataFrame(list(zip(date, mom, cpi)), columns=['date','mom','cpi'])
df['date'] = pd.to_datetime(df['date'])
for i in range(1,len(df),1):
df['cpi'][i] = df['cpi'][(i-1)] * (1 + df['mom'][i])
df['yoy'] = df['cpi'].pct_change(periods=12)
Y-axis values not displaying correctly as can be seen below.
sns.lineplot(
x = 'date',
y = 'yoy',
data = df
)
I think the percentage changes I calculated for the yoy column are the cause of the issue. Because there are no issues if I manually fill in the yoy column.
Thanks in advance.
You can use matplotlib to set the axis scaling, as the difference is really subtle in your data:
import matplotlib.pyplot as plt
ax = plt.gca()
ax.set_ylim([df.yoy.min(numeric_only=True), df.yoy.max(numeric_only=True)])
sns.lineplot(
x = 'date',
y = 'yoy',
data = df,
ax = ax
)
With this the result should be more of a stepping function.
You can use something like the max difference to the mean times 1.01 to set the limits a little better, but this is the idea. You can set the axis ticks using ax.set_yticks(ticks=<list of ticks>) (documentation).
Related
I have written a code that looks like this:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
T = np.array([10.03,100.348,1023.385])
power1 = np.array([100000,86000,73000])
power2 = np.array([1008000,95000,1009000])
df1 = pd.DataFrame(data = {'Size': T, 'Encrypt_Time': power1, 'Decrypt_Time': power2})
exp1= sns.lineplot(data=df1)
plt.savefig('exp1.png')
exp1_smooth= sns.lmplot(x='Size', y='Time', data=df, ci=None, order=4, truncate=False)
plt.savefig('exp1_smooth.png')
That gives me Graph_1:
The Size = x- axis is a constant line but as you can see in my code it varies from (10,100,1000).
How does this produces a constant line? I want to produce a multiline graph with x-axis = Size(T),y- axis= Encrypt_Time and Decrypt_Time (power1 & power2).
Also I wanted to plot a smooth graph of the same graph I am getting right now but it gives me error. What needs to be done to achieve a smooth multi-line graph with x-axis = Size(T),y- axis= Encrypt_Time and Decrypt_Time (power1 & power2)?
I think it not the issue, the line represents for size looks like constant but it NOT.
Can see that values of size in range 10-1000 while the minimum division of y-axis is 20,000 (20 times bigger), make it look like a horizontal line on your graph.
You can try with a bigger values to see the slope clearly.
If you want 'size` as x-axis, you can try below example:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
T = np.array([10.03,100.348,1023.385])
power1 = np.array([100000,86000,73000])
power2 = np.array([1008000,95000,1009000])
df1 = pd.DataFrame(data = {'Size': T, 'Encrypt_Time': power1, 'Decrypt_Time': power2})
fig = plt.figure()
fig = sns.lineplot(data=df1, x='Size',y='Encrypt_Time' )
fig = sns.lineplot(data=df1, x='Size',y='Decrypt_Time' )
I'm fairly new to Python and I'm struggling annotating plots at the minute.
I've come from R so I'm used to the ease of being able to annotate scatterplot points with minimum code.
Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
url = ('https://fbref.com/en/share/nXtrf')
df = pd.read_html(url)[0]
df = df[['Unnamed: 1_level_0', 'Unnamed: 2_level_0', 'Play', 'Perf']].copy()
df.columns = df.columns.droplevel()
df = df[['Player','Squad','Min','SoTA','Saves']]
df = df.drop([25])
df['Min'] = pd.to_numeric(df['Min'])
df['SoTA'] = pd.to_numeric(df['SoTA'])
df['Saves'] = pd.to_numeric(df['Saves'])
df['Min'] = df[df['Min'] > 1600]['Min']
df = df.dropna()
df.plot(x = 'Saves', y = 'SoTA', kind = "scatter")
I've tried numerous ways to annotate this plot. I'd like the points to be annotated with corresponding data from 'Player' column.
I've tried using a label_point function that I've found while trying to find a work around buy I keep getting Key Error 0 on most ways I try.
Any assistance would be great. Thanks.
You could loop through both columns and add a text for each entry. Note that you need to save the ax returned by df.plot(...).
ax = df.plot(x='Saves', y='SoTA', kind="scatter")
for x, y, player in zip(df['Saves'], df['SoTA'], df['Player']):
ax.text(x, y, f'{player}', ha='left', va='bottom')
xmin, xmax = ax.get_xlim()
ax.set_xlim(xmin, xmax + 0.15 * (xmax - xmin)) # some more margin to fit the texts
An alternative is to use the mplcursors library to show an annotation while hovering (or after a click):
import mplcursors
mplcursors.cursor(hover=True)
I'm using Pandas and matplotlib to try to replicate this graph from tableau:
So far, I have this code:
group = df.groupby(["Region","Rep"]).sum()
total_price = group["Total Price"].groupby(level=0, group_keys=False)
total_price.nlargest(5).plot(kind="bar")
Which produces this graph:
It correctly groups the data, but is it possible to get it grouped similar to how Tableau shows it?
You can create some lines and labels using the respective matplotlib methods (ax.text and ax.axhline).
import pandas as pd
import numpy as np; np.random.seed(5)
import matplotlib.pyplot as plt
a = ["West"]*25+ ["Central"]*10+ ["East"]*10
b = ["Mattz","McDon","Jeffs","Warf","Utter"]*5 + ["Susanne","Lokomop"]*5 + ["Richie","Florence"]*5
c = np.random.randint(5,55, size=len(a))
df=pd.DataFrame({"Region":a, "Rep":b, "Total Price":c})
group = df.groupby(["Region","Rep"]).sum()
total_price = group["Total Price"].groupby(level=0, group_keys=False)
gtp = total_price.nlargest(5)
ax = gtp.plot(kind="bar")
#draw lines and titles
count = gtp.groupby("Region").count()
cum = np.cumsum(count)
for i in range(len(count)):
title = count.index.values[i]
ax.axvline(cum[i]-.5, lw=0.8, color="k")
ax.text(cum[i]-(count[i]+1)/2., 1.02, title, ha="center",
transform=ax.get_xaxis_transform())
# shorten xticklabels
ax.set_xticklabels([l.get_text().split(", ")[1][:-1] for l in ax.get_xticklabels()])
plt.show()
I am trying to show time series lines representing an effort amount using matplotlib and pandas.
I've got my DF's to all to overlay in one plot, however when I do python seems to strip the x axis of the date and input some numbers. (I'm not sure where these come from but at a guess, not all days contain the same data so python has reverted to using an index id number). If I plot any one of these they come up with date on the x-axis.
Any hints or solutions to make the x axis show date for the multiple plot would be much appreciated.
This is the single figure plot with time axis:
Code I'm using to plot is
fig = pl.figure()
ax = fig.add_subplot(111)
ax.plot(b342,color='black')
ax.plot(b343,color='blue')
ax.plot(b344,color='red')
ax.plot(b345,color='green')
ax.plot(b346,color='pink')
ax.plot(fi,color='yellow')
plt.show()
This is the multiple plot fig with weird x axis:
One option would be to manually specify the x-axis based on the DataFrame index, and then plot directly using matplotlib.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# make up some data
n = 100
dates = pd.date_range(start = "2015-01-01", periods = n, name = "yearDate")
dfs = []
for i in range(3):
df = pd.DataFrame(data = np.random.random(n)*(i + 1), index = dates,
columns = ["FishEffort"] )
df.df_name = str(i)
dfs.append(df)
# plot it directly using matplotlib instead of through the DataFrame
fig = plt.figure()
ax = fig.add_subplot()
for df in dfs:
plt.plot(df.index,df["FishEffort"], label = df.df_name)
plt.legend()
plt.show()
Another option would be to concatenate your DataFrames and plot using Pandas. If you give your "FishEffort" field the correct label name when loading the data or via DataFrame.rename then the labels will be specified automatically.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
n = 100
dates = pd.date_range(start = "2015-01-01", periods = n, name = "yearDate")
dfs = []
for i in range(3):
df = pd.DataFrame(data = np.random.random(n)*(i + 1), index = dates,
columns = ["DataFrame #" + str(i) ] )
df.df_name = str(i)
dfs.append(df)
df = pd.concat(dfs, axis = 1)
df.plot()
I've found an answer that does what I want, it seems that calling plt.plot wasn't using the date as the x axis, however calling it using the pandas documentation did the trick.
ax = b342.plot(label='342')
b343.plot(ax=ax, label='test')
b344.plot(ax=ax)
b345.plot(ax=ax)
b346.plot(ax=ax)
fi.plot(ax=ax)
plt.show()
I was wondering if anyone knew hwo to change the labels here?
How do I plot a barchart similar to
Clustered bar plot in gnuplot using python matplotlib?
date|name|empid|app|subapp|hours
20140101|A|0001|IIC|I1|2.5
20140101|A|0001|IIC|I2|3
20140101|A|0001|IIC|I3|4
20140101|A|0001|CAR|C1|2.5
20140101|A|0001|CAR|C2|3
20140101|A|0001|CAR|C3|2
20140101|A|0001|CAR|C4|2
Trying to plot the subapp hours by app for the same person. Couldn't see an example in the demo pages of matplotlib.
EDIT: None of the examples cited below seem to work for unequal # of bars for each category as above.
The examples didn't manage unequal # of bars but you can use another approach. I'll post you an example.
Note: I use pandas to manipulate your data, if you don't know about it you should give it a try http://pandas.pydata.org/:
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
df = pd.read_table("data.csv",sep="|")
grouped = df.groupby('app')['hours']
colors = "rgbcmyk"
fig, ax = plt.subplots()
initial_gap = 0.1
start = initial_gap
width = 1.0
gap = 0.05
for app,group in grouped:
size = group.shape[0]
ind = np.linspace(start,start + width, size+1)[:-1]
w = (ind[1]-ind[0])
start = start + width + gap
plt.bar(ind,group,w,color=list(colors[:size]))
tick_loc = (np.arange(len(grouped)) * (width+gap)) + initial_gap + width/2
ax.set_xticklabels([app for app,_ in grouped])
ax.xaxis.set_major_locator(mtick.FixedLocator(tick_loc))
plt.show()
And on data.csv is the data:
date|name|empid|app|subapp|hours
20140101|A|0001|IIC|I1|2.5
20140101|A|0001|IIC|I2|3
20140101|A|0001|IIC|I3|4
20140101|A|0001|CAR|C1|2.5
20140101|A|0001|CAR|C2|3
20140101|A|0001|CAR|C3|2
20140101|A|0001|CAR|C4|2