Develop Reference

Pandas - plot every single column of a Dataframe in a small multiple chart

I have a dataset with 35 features. I want to plot every feature in a small multiple chart type, as this:
Now, I am able to plot features one by one with the following code:
# libraries and data
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Make a data frame
df=pd.read_csv("mydataset.csv")
df.plot(y="feature1")
How can I achieve the result shown in the previous image ? I need something similar to:
# read the data in a Dataframe object
# for each feature (column):
# plot the feature in the final small multiple chart
# render the full final chart
Thank you !

To complete the comment provided by Chris, I would suggest:
Transform your dataframe so all the columns you want to plot are contained in one column, using pd.melt. Let's call the list of columns that you want to plot columns_to_plot
df_for_plot = pd.melt(df, value_vars = columns_to_plot)
Use seaborn FacetGrid to plot it into separated plots:
g = sns.FacetGrid(df_for_plot, col="variable")
g = g.map(plt.plot, "value")

Subplot counts of multiple categorical variables on a single bar chart

I'm trying to create a single barplot from multiple dataframe columns each of which is a categorical variable (all based on the same levels). I want it to show a count of the levels occurring in each column.
The below code achieves what I want, but on 4 different bar plots. I'd like it all to be on one plot, so the bars are side by side (labels/legend would be rad). I'm trying to a get clean, simple solution using matplotlib but so far I can't figure it out. Help?
Thanks!
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
df = pd.DataFrame({"A":['cow','pig','horse','goat','cow'], "B":['cow','pig','horse','cow','goat'], "C":['pig','horse','goat','pig','cow'], "D":['cow','pig','horse','horse','goat'], "E":['pig','horse','goat','cow','goat']})
levels = np.sort(df['A'].unique())
df.A.value_counts()[levels].plot(kind='bar')
df.B.value_counts()[levels].plot(kind='bar')
df.C.value_counts()[levels].plot(kind='bar')
df.D.value_counts()[levels].plot(kind='bar')

You should apply pd.series.value_counts and plot a bar graph, stacked or unstacked.
If you need each column on its own;
df.apply(pd.Series.value_counts).plot(kind='bar')
if you need them stacked;
df.apply(pd.Series.value_counts).plot(kind='bar', stacked=True)

How to plot only one half of a scatter matrix using pandas

I am using pandas scatter_matrix (couldn't get PairgGrid in seaborn to work) to plot all combinations of a set of columns in a pandas frame. Each column as 1000 data points and there are nine columns.
I am using the following code:
pandas.plotting.scatter_matrix(df, alpha=0.2, figsize=(8,8))
I get the figure shown below:
This is nice., However, you'll notice that across the main diagonal I have a mirror image. Is it possible to plot only the lower portion as in the following fake plot I made using paint:

This is probably not the cleanest way to do it, but it works:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
axes = pd.plotting.scatter_matrix(iris, alpha=0.2, figsize=(8,8))
for i in range(np.shape(axes)[0]):
for j in range(np.shape(axes)[1]):
if i < j:
axes[i,j].set_visible(False)

How to create a scatter plot for each dataframe column

I am trying to write some code in order to create an animation of scatter plot data through tine. In order to do this I have a dataset with multiple columns where each column represents a numbered timestep.
I would like the code to cycle through each timestep column for the y axis and use a constant x axis, so that a separate scatter plot is generated for each timestep. I tried to do this by coding a for loop that specifies an incrementing column number for the y axis.
My current code generates three out of seven scatter plots in my sample data but returns the following error:
IndexError: index 9 is out of bounds for axis 0 with size 9
I have tried other similar solutions on stack overflow but that didn't correct my problem.
The data is here if anyone wants to use what I am using: https://www.dropbox.com/s/7vwa0lud44td2ak/test_splot_anim_noTS.csv?dl=0data file
Any help or advice would be much appreciated.
import numpy as np
import pandas a pd
import matplotlib as mpl
import matplotlib.pyplot as plt
data=pd.read_csv("test_splot_anim_noTS.csv")
for n in range (6, 13):
data.plot(kind='scatter', x='metres', y=n)
plt.ylim(-4,4)
plt.savefig('n.jpeg')

data=pd.read_csv("test_splot_anim_noTS.csv")
for column in data.columns[1:]:
data.plot(kind='scatter', x='metres',y=column)
plt.ylim(-4,4)
plt.savefig('{}.jpeg'.format(column))
I may have done it!

panda.DataFrame.plot, single line plot
data=pd.read_csv("test_splot_anim_noTS.csv")
data.set_index('metres', drop=True, inplace=True)
data.plot()
With matplotlib, single plot with all columns:
import matplotlib.pyplot as plt
plt.plot(data)
plt.show()
Separate scatter plots, files saved:
for col in data.columns:
plt.scatter(data.index, data[col])
plt.ylim(-4, 4)
plt.savefig(f'{col}.jpeg')
plt.show()
With Seaborn:
for col in data.columns:
sns.scatterplot(data.index, data[col])
plt.ylim(-4,4)
plt.savefig(f'{col}.jpeg')
plt.show()

Visualizing data for the purpose of data cleaning

I have a column of a data frame with millions of rows (almost 8 million). I want to investigate this column in order to do some data cleaning. The data contained is trip_distance of NYC's yellow taxis.
I tried a simple plotting with sns.distplot() but it doesn't give me a clear plot.
I did try to use a range too: sns.distplot(df['trip_distance']<200, kde=False, bins=10, norm_hist=False), but I got this which again does not look helpful:
Is there a way to understand this column through visualization?

You can try this:->
import pandas as pd
import matplotlib.pyplot as plt
s=pd.read_csv("name.csv",usecols=['col_name'],squeeze=True)
s.plot.bar() #for bar graph
s.plot.hist() #for histogram