# plotting_with_pandas

Let's see some plotting which is generally done with pandas, when I have to do plots I prefer to generally do:

import pandas as pd import matplotlib.pyplot as plt

### Line Plots

If we have a dataframe in which we can plot more columns as lines we can do:

a.plot(x = 'col1', y = ['col2','col3'])

This will plot automatically a figure with a legend and on the x axis we will have the values belonging to col1 while on y axis with different colors we will have the values of col2 and col3.

If we do not specify the parameter 'x', matplotlib will automatically use the dataframe index as 'x'.

By default the plot() function uses as parameter 'kind' the value 'line', so automatically plots a line plot.

### Scatter Plots

We can make a scatter plot of two columns of a dataframe like this:

df.plot(kind='scatter', x='Height', y='Weight')

Now let's say we want to plot more things on the same plot, what we can do is use the parameter 'ax' to refer to the same plot.

For example:

fig, ax = plt.subplots() males.plot(kind='scatter', x='Height', y='Weight', ax=ax, color='blue', alpha=0.3, title='Male & Female Populations') females.plot(kind='scatter', x='Height', y='Weight', ax=ax, color='red', alpha=0.3)

Or another thing we can do is to add to our dataframe a color column and then add the 'c' parameter:

df['Gendercolor'] = df['Gender'].map({'Male': 'blue', 'Female': 'red'}) df.plot(kind='scatter', x='Height', y='Weight', c=df['Gendercolor'], alpha=0.3, title='Male & Female Populations')

We can also specify the value range on the axis with the parameters 'xlim' and 'ylim', like this:

df.plot(kind='scatter', x='col1', y='col2', xlim=(-1.5, 1.5), ylim=(0, 3))

### Histogram Plots

We can plot histograms like this:

df['Height'].plot(kind='hist', bins=50, alpha=0.3, color='blue')

we can also specify a range by doing:

df['Height'].plot(kind='hist', bins=50, alpha=0.3, range = (30,100), color='blue')

We can also have the mean or median line overimposed on an histogram plot, for example by doing:

plt.axvline(males['Height'].mean(), color='blue', linewidth=2) plt.axvline(females['Height'].mean(), color='red', linewidth=2)

#### Plotting the Cumulative Distribution

We can plot the cumulative distribution of a column, like this:

df.column1.plot(kind='hist', bins=100, title='Cumulative distributions', normed=True, cumulative=True, alpha=0.4)

### Plotting an estimate of the Probability Density Function

In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable. Kernel density estimation is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzenâ€“Rosenblatt window method.

df.col1.plot(kind='kde')

### Box Plots

df.column1.plot(kind='box', color = 'red', title='Boxplot')

We can also plot boxplots horizontally like this:

df.plot.box(vert=False, positions=[1, 4, 5, 6, 8]) # here we also specified the positions

color = dict(boxes='DarkGreen', whiskers='DarkOrange', medians='DarkBlue', caps='Gray') df.plot.box(color=color, sym='r+')

### Bar Plots

ds.column_name.plot(kind = 'bar')

### Combination of more plots

fig, ax = plt.subplots(2, 2, figsize=(5, 5)) df.plot(ax=ax[0][0], title='Line plot') df.plot(ax=ax[0][1], style='o', title='Scatter plot') df.plot(ax=ax[1][0], kind='hist', bins=50, title='Histogram') df.plot(ax=ax[1][1], kind='box', title='Boxplot') plt.tight_layout() # this is used in order to not have titles imposed on plots

### Scatter Matrix Plots

We can also plot scatter plots for all the features:

from pandas.plotting import scatter_matrix scatter_matrix(df, alpha=0.2, figsize=(10, 10), diagonal='kde')

This not only allows us to have a lot of plots, but puts on the diagonal the probability density function estimation with the KDE method, we can change this by putting 'hist'.

### Pie Plots

gt01 = df['data1'] > 0.1 piecounts = gt01.value_counts() # Piecounts will have only two values with a specific count piecounts.plot(kind='pie', figsize=(5, 5), explode=[0, 0.15], labels=['<= 0.1', '> 0.1'], autopct='%1.1f%%', shadow=True, startangle=90, fontsize=16)

### Hexbin Plots

df.plot(kind='hexbin', x='x', y='y', bins=100, cmap='rainbow')

### Correlation Plots

In order to view a correlation plot we can do:

import matplotlib.pyplot as plt plt.matshow(df.corr())

### Parallel Coordinates Plot

Parallel coordinates is a plotting technique for plotting multivariate data, see the Wikipedia entry for an introduction. Using parallel coordinates points are represented as connected line segments. Each vertical line represents one attribute. One set of connected line segments represents one data point. Points that tend to cluster will appear closer together.

from pandas.plotting import parallel_coordinates plt.figure() parallel_coordinates(df, 'Title')

The PCA and LDA plots are useful for finding obvious cluster in the data, in the other side scatter plot matrices or parallel coordinate plots show specific behavior of features in a dataset.

### Lag Plots

Lag plots are used to check if a data set or time series is random. Random data should not exhibit any structure in the lag plot. Non-random structure implies that the underlying data are not random.

lag_plot(data)

### Autocorrelation Plots

Autocorrelation plots are often used for checking randomness in time series. This is done by computing autocorrelations for data values at varying time lags.

Autocorrelation plots are often used for checking randomness in time series. This is done by computing autocorrelations for data values at varying time lags. If time series is random, such autocorrelations should be near zero for any and all time-lag separations. If time series is non-random then one or more of the autocorrelations will be significantly non-zero. The horizontal lines displayed in the plot correspond to 95% and 99% confidence bands. The dashed line is 99% confidence band.

### Decorating Plots

We can add lines to indicate points or regions with:

# draws a vertical line plt.axvline(0.2, color='r') # draws an horizontal line plt.axhline(0.5, color='b')

### Visualizing Unstructured Data

In order to visualize unstructured data (e.g., audio, immages, text, ...), we can make use of common packages generally used along with pandas.

#### Audio

For the audio, we can see the signal with:

from scipy.io import wavfile rate, snd = wavfile.read(filename = 'nameoffile.wav') plt.plot(snd)

We can also view the spectrogram by doing:

_ = plt.specgram(snd, NFFT=1024, Fs=44100) plt.ylabel('Frequency (Hz)') plt.xlabel('Time (s)')

#### Images

We can visualize images with:

from PIL import Image import numpy as np img = Image.open('../path/name.jpg') imgarray = np.asarray(img) # This gives us an array imgarray.shape # with this we can understand the shape

At this point we could use ravel() or reshape() to change the size as we wish.

### Setting Plot Options

Once we have a plot with pandas:

hist_plot = ds.colnam1.plot(kind='hist', bins=50) hist_plot.set_xlim(-200,200) hist_plot.set_xlim(-350,350)

Another parameter used when plotting is the label, notice that labels support latex, so we can do:

ax.plot(x, i * x, label='$y = %ix$'.format(i))

Or

bar_plot = ds.colnam1.plot(kind='hist', bins=50) bar_plot.set_xlabel("x label") bar_plot.set_ylabel("y label")

### Other Plotting Utilities

We can instantiate a new plot with a title by doing:

import matplotlib.pyplot as plt plt.figure("title of the figure") # This states, create a plot with 3 figures, and position # them vertically # the general structure is subplot(nrows, ncols, index) # here we will position the figure in the structure 3,1 # at index 1 plt.subplot(311) # To set a scale on y axis we can use plt.ylim([0,350]) ds0.plot() # here we will position the figure in the structure 3,2 # at index 2 # To set a scale on y axis we can use plt.ylim([0,350]) plt.subplot(312) ds1.plot() # here we will position the figure in the structure 3,3 # at index 3 # To set a scale on y axis we can use plt.ylim([0,350]) plt.subplot(313) ds2.plot()

We can also choose a stylesheet, for example we can have the same style of the infamous ggplot package in R with:

import matplotlib.pyplot as plt plt.style.use('ggplot')