I have the statement and I really don't understand the s= part. I know it sets the area of the plot but is it taking the data from pop_2007 and raising it to 1^6 to create the area ?
df.plot(kind='scatter', x='gdp_2007', y='lifeExp_2007', s=df['pop_2007']/1e6)
I'm trying to understand the area of a plot better and the s=
The 's' parameter in the pandas dataframe plot function is changing the size of the markers in your scatter plot. See these two outputs where I change the 's' value from 1 to 100. So right now, your plot is taking the value in the df['pop_2007'] column and dividing it by 1e6 to get your value for the marker size.
#Three lines to make our compiler able to draw:
import sys
import matplotlib
matplotlib.use('Agg')
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('data.csv')
df.plot(kind = 'scatter', x = 'Duration', y = 'Maxpulse', s=1)
plt.show()
#Two lines to make our compiler able to draw:
plt.savefig(sys.stdout.buffer)
sys.stdout.flush()
Plot with s=1
#Three lines to make our compiler able to draw:
import sys
import matplotlib
matplotlib.use('Agg')
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('data.csv')
df.plot(kind = 'scatter', x = 'Duration', y = 'Maxpulse', s=100)
plt.show()
#Two lines to make our compiler able to draw:
plt.savefig(sys.stdout.buffer)
sys.stdout.flush()
Plot with s=100
Test it out here: https://www.w3schools.com/python/pandas/trypandas.asp?filename=demo_pandas_plot_scatter2
Related
I'm trying to use a bar chart to visualize my csv data. The data looks like this:
question,count_1,count_2,count_3,count_4,count_5
Q1,0,0,6,0,0
Q2,6,0,0,0,0
Q3,3,2,1,0,0
Q4,0,0,6,0,0
Q5,6,0,0,0,0
Q6,0,6,0,0,0
Q7,6,0,0,0,0
Q8,0,0,0,5,1
Q9,1,4,0,0,1
Q10,0,0,1,5,0
Here is my code
import pandas as pd
import csv
import matplotlib.pyplot as plt
df = pd.read_csv('example.csv')
ax = df.set_index(['question']).plot.bar(stacked=True)
ax.legend(loc='best')
plt.show()
Which gives me:
What I'm trying to do is flip the x and y axes. I want the bars to be horizontal and y axis to be the questions. I tried to transpose my data frame using:
ax = df.set_index(['question']).T.plot.bar(stacked=True)
but that gives me:
which is not what I want. Can anyone help?
to get the bars horizontally (flip the x and y axis), you need to use barh (horizontal bar). More info here. So, the code would be...
import pandas as pd
import csv
import matplotlib.pyplot as plt
df = pd.read_csv('example.csv')
ax = df.set_index(['question']).plot.barh(stacked=True)
ax.legend(loc='best')
plt.show()
Output plot
I am trying to connect lines based on a specific relationship associated with the points. In this example the lines would connect the players by which court they played in. I can create the basic structure but haven't figured out a reasonably simple way to create this added feature.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df_dict={'court':[1,1,2,2,3,3,4,4],
'player':['Bob','Ian','Bob','Ian','Bob','Ian','Ian','Bob'],
'score':[6,8,12,15,8,16,11,13],
'win':['no','yes','no','yes','no','yes','no','yes']}
df=pd.DataFrame.from_dict(df_dict)
ax = sns.boxplot(x='score',y='player',data=df)
ax = sns.swarmplot(x='score',y='player',hue='win',data=df,s=10,palette=['red','green'])
plt.show()
This code generates the following plot minus the gray lines that I am after.
You can use lineplot here:
sns.lineplot(
data=df, x="score", y="player", units="court",
color=".7", estimator=None
)
The player name is converted to an integer as a flag, which is used as the value of the y-axis, and a loop process is applied to each position on the court to draw a line.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df_dict={'court':[1,1,2,2,3,3,4,4],
'player':['Bob','Ian','Bob','Ian','Bob','Ian','Ian','Bob'],
'score':[6,8,12,15,8,16,11,13],
'win':['no','yes','no','yes','no','yes','no','yes']}
df=pd.DataFrame.from_dict(df_dict)
ax = sns.boxplot(x='score',y='player',data=df)
ax = sns.swarmplot(x='score',y='player',hue='win',data=df,s=10,palette=['red','green'])
df['flg'] = df['player'].apply(lambda x: 0 if x == 'Bob' else 1)
for i in df.court.unique():
dfq = df.query('court == #i').reset_index()
ax.plot(dfq['score'], dfq['flg'], 'g-')
plt.show()
I have a situation with my data. I like the behaviour of .plot() over a data frame. But sometimes it doesn't work, because the frequency of the time index is not an integer.
But reproducing the plot in matplotlib is OK. Just ugly.
The part that bother me the most is the settings of the x axis. The tick frequency and the limits. Is there any easy way that I can reproduce this behaviour in matplotlib?
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# Create Data
f = lambda x: np.sin(0.1*x) + 0.1*np.random.randn(1,x.shape[0])
x = np.arange(0,217,0.001)
y = f(x)
# Create DataFrame
data = pd.DataFrame(y.transpose(), columns=['dp'], index=None)
data['t'] = pd.date_range('2021-01-01 14:32:09', periods=len(data['dp']),freq='ms')
data.set_index('t', inplace=True)
# Pandas plot()
data.plot()
# Matplotlib plot (ugly x-axis)
plt.plot(data.index,data['dp'])
EDIT: Basically, what I want to achieve is a similar spacing in the xtics labels, and the tight margin adjust of the values. Legends and axis title, I can do them
Pandas output
Matplotlib output
Thanks
You can use some matplotlib date utilities:
Figure.autofmt_xdate() to unrotate and center the date labels
Axis.set_major_locator() to change the interval to 1 min
Axis.set_major_formatter() to reformat as %H:%M
fig, ax = plt.subplots()
ax.plot(data.index, data['dp'])
import matplotlib.dates as mdates
fig.autofmt_xdate(rotation=0, ha='center')
ax.xaxis.set_major_locator(mdates.MinuteLocator(interval=1))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
# uncomment to remove the first `xtick`
# ax.set_xticks(ax.get_xticks()[1:])
I can plot multiple histograms in a single plot using pandas but there are few things missing:
How to give the label.
I can only plot one figure, how to change it to layout=(3,1) or something else.
Also, in figure 1, all the bins are filled with solid colors, and its kind of difficult to know which is which, how to fill then with different markers (eg. crosses,slashes,etc)?
Here is the MWE:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
df.groupby('species')['sepal_length'].hist(alpha=0.7,label='species')
plt.legend()
Output:
To change layout I can use by keyword, but can't give them colors
HOW TO GIVE DIFFERENT COLORS?
df.hist('sepal_length',by='species',layout=(3,1))
plt.tight_layout()
Gives:
You can resolve to groupby:
fig,ax = plt.subplots()
hatches = ('\\', '//', '..') # fill pattern
for (i, d),hatch in zip(df.groupby('species'), hatches):
d['sepal_length'].hist(alpha=0.7, ax=ax, label=i, hatch=hatch)
ax.legend()
Output:
In pandas version 1.1.0 you can simply set the legend keyword to true.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = sns.load_dataset('iris')
df.groupby('species')['sepal_length'].hist(alpha=0.7, legend = True)
output image
It's more code, but using pure matplotlib will always give you more control over the plots. For your second case:
import matplotlib.pyplot as plt
import numpy as np
from itertools import zip_longest
# Dictionary of color for each species
color_d = dict(zip_longest(df.species.unique(),
plt.rcParams['axes.prop_cycle'].by_key()['color']))
# Use the same bins for each
xmin = df.sepal_length.min()
xmax = df.sepal_length.max()
bins = np.linspace(xmin, xmax, 20)
# Set up correct number of subplots, space them out.
fig, ax = plt.subplots(nrows=df.species.nunique(), figsize=(4,8))
plt.subplots_adjust(hspace=0.4)
for i, (lab, gp) in enumerate(df.groupby('species')):
ax[i].hist(gp.sepal_length, ec='k', bins=bins, color=color_d[lab])
ax[i].set_title(lab)
# same xlim for each so we can see differences
ax[i].set_xlim(xmin, xmax)
I am trying to make a contour plot from a csv file. I would like the first column to be the x axis, the first row (with has values) to be the y, and then the rest of the matrix is what should be contoured, see the basic example in the figure below.
Simple table example
What I am really struggling is to get that first row to be the y axis, and then how to define that set of values so that they can be called into the contourf function. Any help would be very much appreciated as I am very new to python and am really don't know where to start with this problem.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import csv
import pandas as pd
import numpy as np
from csv import reader
from matplotlib import cm
f = pd.read_csv('/trialforplot.csv',dayfirst=True,index_col=0)
x = f.head()
y = f.columns
X,Y = np.meshgrid(x,y)
z=(x,y)
z=np.array(z)
Z=z.reshape((len(x),len(y)))
plt.contour(Y,X,Z)
plt.colorbar=()
plt.xlabel('Time')
plt.ylable('Particle Size')
plt.show()
I'm stuck at defining the z values and getting my contour plot plotting.