Develop Reference

Bar plot and coloured categorical variable

I have a dataframe with 3 variables:
data= [["2019/oct",10,"Approved"],["2019/oct",20,"Approved"],["2019/oct",30,"Approved"],["2019/oct",40,"Approved"],["2019/nov",20,"Under evaluation"],["2019/dec",30,"Aproved"]]
df = pd.DataFrame(data, columns=['Period', 'Observations', 'Result'])
I want a barplot grouped by the Period column, showing all the values ​​contained in the Observations column and colored with the Result column.
How can I do this?
I tried the sns.barplot, but it joined the values in Observations column in just one bar(mean of the values).
sns.barplot(x='Period',y='Observations',hue='Result',data=df,ci=None)
Plot output

Assuming that you want one bar for each row, you can do as follows:
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
result_cat = df["Result"].astype("category")
result_codes = result_cat.cat.codes.values
cmap = plt.cm.Dark2(range(df["Result"].unique().shape[0]))
patches = []
for code in result_cat.cat.codes.unique():
cat = result_cat.cat.categories[code]
patches.append(mpatches.Patch(color=cmap[code], label=cat))
df.plot.bar(x='Period',
y='Observations',
color=cmap[result_codes],
legend=False)
plt.ylabel("Observations")
plt.legend(handles=patches)

If you would like it grouped by the months, and then stacked, please use the following (note I updated your code to make sure one month had more than one status), but not sure I completely understood your question correctly:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
data= [["2019/oct",10,"Approved"],["2019/oct",20,"Approved"],["2019/oct",30,"Approved"],["2019/oct",40,"Under evaluation"],["2019/nov",20,"Under evaluation"],["2019/dec",30,"Aproved"]]
df = pd.DataFrame(data, columns=['Period', 'Observations', 'Result'])
df.groupby(['Period', 'Result'])['Observations'].sum().unstack('Result').plot(kind='bar', stacked=True)

Python plot bar chart with group

I have the following dataframe:
I'm trying to plot a bar chart, with x as 'config names', y as 'value', and one bar per month (one bin per month). I'm not sure how to do this, any ideas?

If you have your data in a pandas DataFrame (let's say df), it's rather easy:
import seaborn as sns
sns.barplot(x='config names', y='value', data='df')
I'm not sure what you mean by one bin per month. The bins here are your x axis.
If you mean you want to split different months into different bins then you should just add them to the hue parameter.
import seaborn as sns
sns.barplot(x='config names', y='value', data='df', hue='month')

I may not understand what you ask but it looks like this
So I suggest you do a pivot table with your dataframe.
Let's say your dataframe variable name is df, can you try this :
import pandas as pd
import numpy as np
pt_df = pd.pivot_table(
df,
values=['value'],
columns=['month'],
aggfunc=np.sum
).plot(kind='bar')

Plotting data with categorical x and y axes in python

I have a list of case and control samples along with the information about what characteristics are present or absent in each of them. A dataframe including the information can be generated by Pandas:
import pandas as pd
df={'Patient':[True,True,False],'Control':[False,True,False]} # Presence/absence data for three genes for each sample
df=pd.DataFrame(df)
df=df.transpose()
df.columns=['GeneA','GeneB','GeneC']
I need to visualize this data as a dotplot/scatterplot in the way that both of the x and y axis to be categorical and presence/absence to be coded by different shapes. Something like following:
Patient| x x -
Control| - x -
__________________
GeneA GeneB GeneC
I am new to Matplotlib/seaborn and I can plot simple line plots and scatter plots. But searching online I could not find any instructions or plot similar to what I need here.

A quick way would be:
import pandas as pd
import matplotlib.pyplot as plt
df={'Patient':[1,1,0],'Control':[0,1,0]} # Presence/absence data for three genes for each sample
df=pd.DataFrame(df)
df=df.transpose()
df.columns=['GeneA','GeneB','GeneC']
heatmap = plt.imshow(df)
plt.xticks(range(len(df.columns.values)), df.columns.values)
plt.yticks(range(len(df.index)), df.index)
cbar = plt.colorbar(mappable=heatmap, ticks=[0, 1], orientation='vertical')
# vertically oriented colorbar
cbar.ax.set_yticklabels(['Absent', 'Present'])
Thanks to #DEEPAK SURANA for adding labels to the colorbar.

I searched the pyplot documentation and could not find a scatter or dot plot exactly like you described. Here is my take on creating a plot that illustrates what you want. The True records are blue and the False records are red.
# creating dataframe and extra column because index is not numeric
import pandas as pd
df={'Patient':[True,True,False],
'Control':[False,True,False]}
df=pd.DataFrame(df)
df=df.transpose()
df.columns=['GeneA','GeneB','GeneC']
df['level'] = [i for i in range(0, len(df))]
print(df)
# plotting the data
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10,6))
for idx, gene in enumerate(df.columns[:-1]):
df_gene = df[[gene, 'level']]
cList = ['blue' if x == True else 'red' for x in df[gene]]
for inr_idx, lv in enumerate(df['level']):
ax.scatter(x=idx, y=lv, c=cList[inr_idx], s=20)
fig.tight_layout()
plt.yticks([i for i in range(len(df.index))], list(df.index))
plt.xticks([i for i in range(len(df.columns)-1)], list(df.columns[:-1]))
plt.show()

Something like this might work
import pandas as pd
import numpy as np
from matplotlib.ticker import FixedLocator
df={'Patient':[1,1,0],'Control':[0,1,0]} # Presence/absence data for three genes for each sample
df=pd.DataFrame(df)
df=df.transpose()
df.columns=['GeneA','GeneB','GeneC']
plot = df.T.plot()
loc = FixedLocator([0,1,2])
plot.xaxis.set_major_locator(loc)
plot.xaxis.set_ticklabels(df.columns)
look at https://matplotlib.org/examples/pylab_examples/major_minor_demo1.html
and https://matplotlib.org/api/ticker_api.html
I think you have to convert the boolean values to zeros and ones to make it work. Someting like df.astype(int)

Avoid plotting missing values in Seaborn

Problem: I have timeseries data of several days and I use sns.FacetGrid function of Seaborn python library to plot this data in facet form. In several cases, I found that mentioned seaborn function plots consecutive missing values (nan values) between two readings with a continuous line. While as matplotlib shows missing values as a gap, which makes sense. A demo example is as
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# create timeseries data for 3 days such that day two contains NaN values
time_duration1 = pd.date_range('1/1/2018', periods=24,freq='H')
data1 = np.random.randn(len(time_duration1))
ds1 = pd.Series(data=data1,index=time_duration1)
time_duration2 = pd.date_range('1/2/2018',periods=24,freq='H')
data2 = [float('nan')]*len(time_duration2)
ds2 = pd.Series(data=data2,index=time_duration2)
time_duration3 = pd.date_range('1/3/2018', periods=24,freq='H')
data3 = np.random.randn(len(time_duration3))
ds3 = pd.Series(data=data3,index=time_duration3)
# combine all three days series and then convert series into pandas dataframe
DS = pd.concat([ds1,ds2,ds3])
DF = DS.to_frame()
DF.plot()
It results into following plot
Above Matplotlib plot shows missing values with a gap.
Now let us prepare same data for seaborn function as
DF['col'] = np.ones(DF.shape[0])# dummy column but required for facets
DF['timestamp'] = DF.index
DF.columns = ['data_val','col','timestamp']
g = sns.FacetGrid(DF,col='col',col_wrap=1,size=2.5)
g.map_dataframe(plt.plot,'timestamp','data_val')
See, how seaborn plot shows missing data with a line. How should I force seaborn to not plot nan values with such a line?
Note: This is a dummy example, and I need facet grid in any case to plot my data.

FacetGrid by default removes nan from the data. The reason is that some functions inside seaborn would not work properly with nans (especially some of the statistical function, I'd say).
In order to keep the nan values in the data, use the dropna=False argument to FacetGrid:
g = sns.FacetGrid(DF,... , dropna=False)

Plot a pandas dataframe with vertical lines

I want to plot a dataframe where each data point is not represented as a point but a vertical line from the zero axis like :
df['A'].plot(style='xxx')
where xxx is the style I need.
Also ideally i would like to be able to color each bar based on the values in another column in my dataframe.
I precise that my x axis values are numbers and are not equally spaced.

The pandas plotting tools are convenient wrappers to matplotlib. There is no way I know of to get the functionality you want directly via pandas.
You can get it in a few lines of matplotlib. Most of the code is to do the colour mapping:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.colors as colors
import matplotlib.cm as cmx
#make the dataframe
a = np.random.rand(100)
b = np.random.ranf(100)
df = pd.DataFrame({'a': a, 'b': b})
# do the colour mapping
c_norm = colors.Normalize(vmin=min(df.b), vmax=max(df.b))
scalar_map = cmx.ScalarMappable(norm=c_norm, cmap=plt.get_cmap('jet'))
color_vals = [scalar_map.to_rgba(val) for val in df.b]
# make the plot
plt.vlines(df.index, np.zeros_like(df.a), df.a, colors=color_vals)
I've used the DataFrame index for the x axis values but there is no reason that you could not use irregularly spaced x values.