I have been trying to plot some time series graphs using the pandas dataframe plot function. I was trying to add markers at some arbitrary points on the plot to show anomalous points. The code I used :
df1 = pd.DataFrame({'Entropy Values' : MeanValues}, index=DateRange)
df1.plot(linestyle = '-')
I have a list of Dates on which I need to add markers.Such as:
Dates = ['15:45:00', '15:50:00', '15:55:00', '16:00:00']
I had a look at this link matplotlib: Set markers for individual points on a line. Does DF.plot have a similar functionality?
I really appreciate the help. Thanks!
DataFrame.plot passes all keyword arguments it does not recognize to the matplotlib plotting method. To put markers at a few points in the plot you can use the markevery argument. Here is an example:
import pandas as pd
df = pd.DataFrame({'A': range(10), 'B': range(10)}).set_index('A')
df.plot(linestyle='-', markevery=[1, 5, 7, 8], marker='o', markerfacecolor='r')
In your case, you would have to do something like
df1.plot(linestyle='-', markevery=Dates, marker='o', markerfacecolor='r')
Related
When using seaborn, is there a way I can include multiple variables (columns) for the hue parameter? Another way to ask this question would be how can I group my data by multiple variables before plotting them on a single x,y axis plot?
I want to do something like below. However currently I am not able to specify two variables for the hue parameter.:
sns.relplot(x='#', y='Attack', hue=['Legendary', 'Stage'], data=df)
For example, assume I have a pandas DataFrame like below containing an a Pokemon database obtained via this tutorial.
I want to plot on the x-axis the pokedex #, and the y-axis the Attack. However, I want to data to be grouped by both Stage and Legendary. Using matplotlib, I wrote a custom function that groups the dataframe by ['Legendary','Stage'], and then iterates through each group for the plotting (see results below). Although my custom function works as intended, I was hoping this can be achieved simply by seaborn. I am guessing there must be other people what have attempted to visualize more than 3 variables in a single plot using seaborn?
fig, ax = plt.subplots()
grouping_variables = ['Stage','Legendary']
group_1 = df.groupby(grouping_variables)
for group_1_label, group_1_df in group_1:
ax.scatter(group_1_df['#'], group_1_df['Attack'], label=group_1_label)
ax_legend = ax.legend(title=grouping_variables)
Edit 1:
Note: In the example I provided, I grouped the data by obly two variables (ex: Legendary and Stage). However, other situations may require arbitrary number of variables (ex: 5 variables).
You can leverage the fact that hue accepts either a column name, or a sequence of the same length as your data, listing the color categories to assign each data point to. So...
sns.relplot(x='#', y='Attack', hue='Stage', data=df)
... is basically the same as:
sns.relplot(x='#', y='Attack', hue=df['Stage'], data=df)
You typically wouldn't use the latter, it's just more typing to achieve the same thing -- unless you want to construct a custom sequence on the fly:
sns.relplot(x='#', y='Attack', data=df,
hue=df[['Legendary', 'Stage']].apply(tuple, axis=1))
The way you build the sequence that you pass via hue is entirely up to you, the only requirement is that it must have the same length as your data, and if an array-like, it must be one-dimensional, so you can't just pass hue=df[['Legendary', 'Stage']], you have to somehow concatenate the columns into one. I chose tuple as the simplest and most versatile way, but if you want to have more control over the formatting, build a Series of strings. I'll save it into a separate variable here for better readability and so that I can assign it a name (which will be used as the legend title), but you don't have to:
hue = df[['Legendary', 'Stage']].apply(
lambda row: f"{row.Legendary}, {row.Stage}", axis=1)
hue.name = 'Legendary, Stage'
sns.relplot(x='#', y='Attack', hue=hue, data=df)
To use hue of seaborn.relplot, consider concatenating the needed groups into a single column and then run the plot on new variable:
def run_plot(df, flds):
# CREATE NEW COLUMN OF CONCATENATED VALUES
df['_'.join(flds)] = pd.Series(df.reindex(flds, axis='columns')
.astype('str')
.values.tolist()
).str.join('_')
# PLOT WITH hue
sns.relplot(x='#', y='Attack', hue='_'.join(flds), data=random_df, aspect=1.5)
plt.show()
plt.clf()
plt.close()
To demonstrate with random data
Data
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
### DATA
np.random.seed(22320)
random_df = pd.DataFrame({'#': np.arange(1,501),
'Name': np.random.choice(['Bulbasaur', 'Ivysaur', 'Venusaur',
'Charmander', 'Charmeleon'], 500),
'HP': np.random.randint(1, 100, 500),
'Attack': np.random.randint(1, 100, 500),
'Defense': np.random.randint(1, 100, 500),
'Sp. Atk': np.random.randint(1, 100, 500),
'Sp. Def': np.random.randint(1, 100, 500),
'Speed': np.random.randint(1, 100, 500),
'Stage': np.random.randint(1, 3, 500),
'Legend': np.random.choice([True, False], 500)
})
Plots
run_plot(random_df, ['Legend', 'Stage'])
run_plot(random_df, ['Legend', 'Stage', 'Name'])
In seaborn's scatterplot(), you can combine both a hue= and a style= parameter to produce different markers and different colors for each combinations
example (taken verbatim from the documentation):
tips = sns.load_dataset("tips")
ax = sns.scatterplot(x="total_bill", y="tip", data=tips)
ax = sns.scatterplot(x="total_bill", y="tip",
hue="day", style="time", data=tips)
I'm trying to use seaborn dataframe functionality (e.g. passing column names to x, y and hue plot parameters) for my timeseries (in pandas datetime format) plots.
x should come from a timeseries column(converted from a pd.Series of strings with pd.to_datetime)
y should come from a float column
hue comes from a categorical column that I calculated.
There are multiple streams in the same series that I am trying to separate (and use the hue for separating them visually), and therefore they should not be connected by a line (like in a scatterplot)
I have tried the following plot types, each with a different problem:
sns.scatterplot: gets the plotting right and the labels right bus has problems with the xlimits, and I could not set them right with plt.xlim() using data.Dates.min and data.Dates.min
sns.lineplot: gets the limits and the labels right but I could not find a setting to disable the lines between the individual datapoints like in matplotlib. I tried the setting the markers and the dashes parameters to no avail.
sns.stripplot: my last try, plotted the datapoints correctly and got the xlimits right but messed the labels ticks
Example input data for easy reproduction:
dates = pd.to_datetime(('2017-11-15',
'2017-11-29',
'2017-12-15',
'2017-12-28',
'2018-01-15',
'2018-01-30',
'2018-02-15',
'2018-02-27',
'2018-03-15',
'2018-03-27',
'2018-04-13',
'2018-04-27',
'2018-05-15',
'2018-05-28',
'2018-06-15',
'2018-06-28',
'2018-07-13',
'2018-07-27'))
values = np.random.randn(len(dates))
clusters = np.random.randint(1, size=len(dates))
D = {'Dates': dates, 'Values': values, 'Clusters': clusters}
data = pd.DataFrame(D)
To each of the functions I am passing the same arguments:
sns.OneOfThePlottingFunctions(x='Dates',
y='Values',
hue='Clusters',
data=data)
plt.show()
So to recap, what I want is a plot that uses seaborn's pandas functionality, and plots points(not lines) with correct x limits and readable x labels :)
Any help would be greatly appreciated.
ax = sns.scatterplot(x='Dates', y='Values', hue='Clusters', data=data)
ax.set_xlim(data['Dates'].min(), data['Dates'].max())
I have a Series that I would like to plot as a bar chart: pd.Series([-4,2, 3,3, 4,5,9,20]).value_counts()
Since I have many bars I only want to display some (equidistant) ticks.
However, unless I actively work against it, pyplot will print the wrong labels. E.g. if I leave out set_xticklabels in the code below I get
where every element from the index is taken and just displayed with the specified distance.
This code does what I want:
import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
s = pd.Series([-4,2, 3,3, 4,5,9,20]).value_counts().sort_index()
mi,ma = min(s.index), max(s.index)
s = s.reindex(range(mi,ma+1,1), fill_value=0)
distance = 10
a = s.plot(kind='bar')
condition = lambda t: int(t[1].get_text()) % 10 == 0
ticks_,labels_=zip(*filter(condition, zip(a.get_xticks(), a.get_xticklabels())))
a.set_xticks(ticks_)
a.set_xticklabels(labels_)
plt.show()
But I still feel like I'm being unnecessarily clever here. Am I missing a function? Is this the best way of doing that?
Consider not using a pandas bar plot in case you intend to plot numeric values; that is because pandas bar plots are categorical in nature.
If instead using a matplotlib bar plot, which is numeric in nature, there is no need to tinker with any ticks at all.
s = pd.Series([-4,2, 3,3, 4,5,9,20]).value_counts().sort_index()
plt.bar(s.index, s)
I think you overcomplicated it. You can simply use the following. You just need to find the relationship between the ticks and the ticklabels.
a = s.plot(kind='bar')
xticks = np.arange(0, max(s)*10+1, 10)
plt.xticks(xticks + abs(mi), xticks)
I am trying to create a bar plot using pandas. I have the following code:
import pandas as pd
indexes = ['Strongly agree', 'Agree', 'Neutral', 'Disagree', 'Strongly disagree']
df = pd.DataFrame({'Q7': [10, 11, 1, 0, 0]}, index=indexes)
df.plot.bar(indexes, df['Q7'].values)
By my reckoning this should work but I get a weird KeyError: 'Strongly agree' thrown at me. I can't figure out why this won't work.
By invoking plot as a Pandas method, you're referring to the data structures of df to make your plot.
The way you have it set up, with index=indexes, your bar plot's x values are stored in df.index. That's why Wen's suggestion in the comments to just use df.plot.bar() will work, as Pandas automatically looks to use df.index as the x-axis in this case.
Alternately, you can specify column names for x and y. In this case, you can move indexes into a column with reset_index() and then call the new index column explicitly:
df.reset_index().plot.bar(x="index", y="Q7")
Either approach will yield the correct plot:
I am using pandas for graphing data for a cluster of nodes. I find that pandas is repeating color values for the different series, which makes them indistinguishable.
I tried giving custom color values like this and passed the my_colors to the colors field in plot:
my_colors = []
for node in nodes_list:
my_colors.append(rand_color())
rand_color() is defined as follows:
def rand_color():
from random import randrange
return "#%s" % "".join([hex(randrange(16, 255))[2:] for i in range(3)])
But here also I need to avoid color values that are too close to distinguish. I sometimes have as many as 60 nodes (series). Most probably a hard-coded list of color values would be best option?
You can get a list of colors from any colormap defined in Matplotlib, and even custom colormaps, by:
>>> import matplotlib.pyplot as plt
>>> colors = plt.cm.Paired(np.linspace(0,1,60))
Plotting an example with these colors:
>>> plt.scatter( range(60), [0]*60, color=colors )
<matplotlib.collections.PathCollection object at 0x04ED2830>
>>> plt.axis("off")
(-10.0, 70.0, -0.0015, 0.0015)
>>> plt.show()
I found the "Paired" colormap to be especially useful for this kind of things, but you can use any other available or custom colormap.