I've used pandas.read_csv to generate a 1000-row dataframe with 32 columns. I'm looking to plot a histogram or bar chart (depending on data type) of each column. For columns of type 'int64', I've tried doing matplotlib.pyplot.hist(df['column']) and df.hist(column='column'), as well as calling matplotlib.pyplot.hist on df['column'].values and df['column'].to_numpy(). Weirdly, nthey all take areally long time (>30s) and when I've allowed them to complet, I get unit-height bars in multiple colors, as if there's some sort of implicit grouping and they're all being separated into different groups. Any ideas about what I can do to get a normal histogram? Unfortunately I closed the charts so I can't show you an example right now.
Edit - this seems to be a much bigger problem with Int columns, and casting them to float fixes the problem.
Follow these two steps:
import the Histogram class from the Matplotlib library
use the "plot" method, which will accept a dataframe as argument
import matplotlib.pyplot as plt
plt.hist(df['column'], color='blue', edgecolor='black', bins=int(45/1))
Here's the source.
Related
I am attempting to create a simple hbar() chart on two columns [project, bug_count]. Sample dataframe follows:
df = pd.DataFrame({'project': ['project1', 'project2', 'project3', 'project4'],
'bug_count': [43683, 31647, 27494, 24845]})
When attempting to render any chart: scatter, circle, vbar etc... I get a blank chart.
This very simple code snippet shows an empty viz. This example shows a f.circle() just for demonstration, I'm actually trying to implement a f.hbar().
from bokeh.io import show, output_notebook
from bokeh.plotting import figure
f = figure()
f.circle(df['project'], df['bug_count'],size = 10)
show(f)
The values of df['project'] are strings, i.e. categorical values, not numbers. Categorical ranges must be explicitly provided, since you are the only person who possess the knowledge of what order the arbitrary factors should appear in on the axis. Something like
p = figure(x_range=sorted(set(df['project'])))
There is an entire chapter in the User's Guide devoted to Handling Categorical Data, with many complete examples (including many bar charts) that you can refer to.
I have the following Dataset and I wanna create a plot, which to columns compares with each other.
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
ds=pd.read_csv('h-t-t-p-:bit.ly/uforeports') #My DataSet
ds.head(5) # Only the fist 5 rows to show
ds1= ds.head(4).drop(['Colors Reported','State'],axis=1) # Droping of unnecesssary rows
print(ds1)
Now I wanna compare "City" and "Shape Reported" with help of plotting. I found something with Pandas but this is not so elegant!
x=ds.loc[0:100,['State']]
y=ds.loc[0:100,['Shape Reported']]
x.apply(pd.value_counts).plot(kind='bar', subplots=True)
y.apply(pd.value_counts).plot(kind='bar', subplots=True)
Do you know a better solution with Matplotlib to this problem?
This is what I want
It's not exactly clear how you want to compare them.
The simplest way of drawing a bar chart is:
df['State'].value_counts().plot.bar()
df['Shape Reported'].value_counts().plot.bar()
If you just want to do it for the first 100 rows as in your example, just add head(100):
df['State'].head(100).value_counts().plot.bar()
df['Shape Reported'].head(100).value_counts().plot.bar()
EDIT:
To compare the two values you can plot a bivariate distribution plot. This is easily done with seaborn:
import seaborn
sns.displot(df,x='State', y='Shape Reported', height=6, aspect=1.33)
Result:
The method plt.hist() in pyplot has a way to create a 'step-like' plot style when calling
plt.hist(data, histtype='step')
but the 'ordinary' methods that plot raw data without processing (plt.plot(), plt.scatter(), etc.) apparently do not have style options to obtain the same result. My goal is to plot a given set of points using that style, without making histogram of these points.
Is that achievable with standard library methods for plotting a given 2-D set of points?
I also think that there is at least one hack (generating a fake distribution which would have histogram equal to our data) and a 'low-level' solution to draw each segment manually, but none of these ways seems favorable.
Maybe you are looking for drawstyle="steps".
import numpy as np; np.random.seed(42)
import matplotlib.pyplot as plt
data = np.cumsum(np.random.randn(10))
plt.plot(data, drawstyle="steps")
plt.show()
Note that this is slightly different from histograms, because the lines do not go to zero at the ends.
I can use DataFrameGroupBy.boxplot(...) to create a boxplot in the following way:
In [15]: df = pd.DataFrame({"gene_length":[100,100,100,200,200,200,300,300,300],
...: "gene_id":[1,1,1,2,2,2,3,3,3],
...: "density":[0.4,1.1,1.2,1.9,2.0,2.5,2.2,3.0,3.3],
...: "cohort":["USA","EUR","FIJ","USA","EUR","FIJ","USA","EUR","FIJ"]})
In [17]: df.groupby("cohort").boxplot(column="density",by="gene_id")
In [18]: plt.show()
This produces the following image:
This is exactly what I want, except instead of making three subplots, I want all the plots to be in one plot (with different colors for USA, EUR, and FIJ). I've tried
In [17]: df.groupby("cohort").boxplot(column="density",subplots=False,by="gene_id")
but it produces the error
KeyError: 'gene_id'
I think the problem has something to do with the fact that by="gene_id" is a keyword sent to the matplotlib boxplot method. If someone has a better way of producing the plot I am after, perhaps by using DataFrame.boxplot(?) instead, please respond here. Thanks so much!
To use the pure pandas functions, I think you should not GroupBy before calling boxplot, but instead, request to group by certain columns in the call to boxplot on the DataFrame itself:
df.boxplot(column='density',by=['gene_id','cohort'])
To get a better-looking result, you might want to consider using the Seaborn library. It is designed to help precisely with this sort of tasks:
sns.boxplot(data=df,x='gene_id',y='density',hue='cohort')
EDIT to take into account comment below
If you want to have each of your cohort boxplots stacked/superimposed for each gene_id, it's a bit more complicated (plus you might end up with quite an ugly output). You cannot do this using Seaborn, AFAIK, but you could with pandas directly, by using the position= parameter to boxplot (see doc). The catch it to generate the correct sequence of positions to place the boxplots where you want them, but you'll have to fix the tick labels and the legend yourself.
pos = [i for i in range(len(df.gene_id.unique())) for _ in range(len(df.cohort.unique()))]
df.boxplot(column='density',by=['gene_id','cohort'],positions=pos)
An alternative would be to use seaborn.swarmplot instead of using boxplot. A swarmplot plots every point instead of the synthetic representation of boxplots, but you can use the parameter split=False to get the points colored by cohort but stacked on top of each other for each gene_id.
sns.swarmplot(data=df,x='gene_id',y='density',hue='cohort', split=False)
Without knowing the actual content of your dataframe (number of points per gene and per cohort, and how separate they are in each cohort), it's hard to say which solution would be the most appropriate.
Assuming I have the following array: [1,1,1,2,2,40,60,70,75,80,85,87,95] and I want to create a histogram out of it based on the following bins - x<=2, [3<=x<=80], [x>=81].
If I do the following: arr.hist(bins=(0,2,80,100)) I get the bins to be at different widths (based on their x range). I want them to represent different size ranges but appear in the histogram at the same width. Is it possible in an elegant way?
I can think of adding a new column for this (holding the bin id that will be calculated based on the boundaries I want) but don't really like this solution..
Thanks!
Sounds like you want a bar graph; You could use bar:
import numpy as np
import matplotlib.pyplot as plt
arr=np.array([1,1,1,2,2,40,60,70,75,80,85,87,95])
h=np.histogram(arr,bins=(0,2,80,100))
plt.bar(range(3),h[0],width=1)
xlab=['x<=2', '3<=x<=80]', 'x>=81']
plt.xticks(arange(0.5,3.5,1),xlab)