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Box and whisker plot on multiple columns

I am trying to make a Box and Whisker plot on my dataset that looks something like this -
& the chart I'm trying to make
My current lines of code are below -
import seaborn as sns
import matplotlib.pyplot as plt
d = df3.boxplot(column = ['Northern California','New York','Kansas','Texas'], by = 'Banner')
d
Thank you

I've recreated a dummy version of your dataset:
import numpy as np
import pandas as pd
dictionary = {'Banner':['Type1']*10+['Type2']*10,
'Northen_californina':np.random.rand(20),
'Texas':np.random.rand(20)}
df = pd.DataFrame(dictionary)
What you need is to melt your dataframe (unpivot) in orther to have the information of geographical zone stored in a column and not as column name. You can use pandas.melt method and specify all the columns you want to put in your boxplot in the value_vars argument.
With my dummy dataset you can do this:
df = pd.melt(df,id_vars=['Banner'],value_vars=['Northen_californina','Texas'],
var_name='zone', value_name='amount')
Now you can apply a boxplot using the hue argument:
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(9,9)) #for a bigger image
sns.boxplot(x="Banner", y="amount", hue="zone", data=df, palette="Set1")

Plotting 2 columns of a csv with matplotlib error

I am trying to make a simple bar graph out of a 2 column CSV file. One column is the x axis names, the other column is the actual data which will be used for the bars. The CSV looks like this:
count,team
21,group1
15,group2
63,group3
22,group4
42,group5
72,group6
21,group7
23,group8
24,group9
31,group10
32,group11
I am using this code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv("sampleData.csv",sep=",").set_index('count')
d = dict(zip(df.index,df.values.tolist()))
df.plot.bar(x = 'count', y = 'team')
print(d)
However, I get an error
KeyError: 'count' from this line :
df.plot.bar(x = 'count', y = 'team')
I don't understand how there is an error for something that exists.

When you set the count as index, you just have a single column left in your DataFrame, i.e., team. Don't set the count as index and switch the order of x and y values for plotting the bar chart
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv("sampleData.csv", sep=",")
df.plot.bar(x = 'team', y = 'count')
Matplotlib solution
plt.bar(df['team'], df['count'])
plt.xticks(rotation=45) # Just rotating for better visualizaton

matplotlib dataframe x axis date issue

import sys
import ConfigParser
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime as DT
import bokeh
sys.path.extend(['..\..\myProj\SOURCE'])
fullfilepath = "../../myProj/SOURCE/" + 'myparts.txt'
ohg_df = pd.read_csv(fullfilepath, sep="\t" )
temp_df = temp_df[['as_on_date', 'ohg_qty']]
temp_df = temp_df.sort(['as_on_date'], ascending=[1])
temp_df.set_index('as_on_date')
plt.plot(temp_df.index, temp_df.ohg_qty)
plt.show()
This is my dataframe after importing.
I am trying to plot the line graph with x axis as date mentioned in the dataframe.
Can someone guide me... I am new to pandas.
dataframe picture
output pitcure

Easier:
# Set index directly
ohg_df = pd.read_csv(fullfilepath, sep="\t", index='as_on_date')
# Convert string index to dates
ohg_df.index = pd.to_datetime(ohg_df.index)
# Get a column and plot it (taking a column keeps the index)
plt.plot(ohg_df.ohg_qty)

plot sensor boolean data matplotlib

I have data from two sensors that I want to visualize. Both sensors take only 0/1 values. How can I change the xaxis labels to show the time series and y axis should have 2 labels 0 and 1 representing the value of sensors along the time series.
import pandas as pd
import matplotlib.pyplot as plt
def drawgraph(inputFile):
df=pd.read_csv(inputFile)
fig=plt.figure()
ax=fig.add_subplot(111)
y = df[['sensor1']]
x=df.index
plt.plot(x,y)
plt.show()

You should have explained what you tried before asking a question for this to be meaningful. Anyway, below is the example.
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
trange = pd.date_range("11:00", "21:30", freq="30min")
df = pd.DataFrame({'time':trange,'sensor1':np.round(np.random.rand(len(trange))),\
'sensor2':np.round(np.random.rand(len(trange)))})
df = df.set_index('time')
df.plot(yticks=[0,1],ylim=[-0.1,1.1],style={'sensor1':'ro','sensor2':'bx'})

forming histogram plots in python

suppose I want to plot 2 histogram subplots on the same window in python, one below the next. The data from these histograms will be read from a file containing a table with attributes A and B.
In the same window, I need a plot of A vs the number of each A and a plot of B vs the number of each B - directly below the plot of A. so suppose the attributes were height and weight, then we'd have a graph of height and number of people with said height and below it a separate graph of weight and number of people with said weight.
import numpy as np; import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
frame = pd.read_csv('data.data', header=None)
subplot.hist(frame['A'], frame['A.count()'])
subplot.hist(frame['B'], frame['B.count()'])
Thanks for any help!

Using pandas you can make histograms like this:
import numpy as np; import pandas as pd
import matplotlib.pyplot as plt
frame = pd.read_csv('data.csv')
frame.hist(layout = (2,1))
plt.show()
I'm confused by the second part of the question. Do you want four separate subplots?

You can do this:
import numpy as np
import numpy.random
import pandas as pd
import matplotlib.pyplot as plt
#df = pd.read_csv('data.data', header=None)
df = pd.DataFrame({'A': numpy.random.random_integers(0,10,30),
'B': numpy.random.random_integers(0,10,30)})
print df['A']
ax1 = plt.subplot(211)
ax1.set_title('A')
ax1.set_ylabel('number of people')
ax1.set_xlabel('height')
ax2 = plt.subplot(212)
ax2.set_title('B')
ax2.set_ylabel('number of people')
ax2.set_xlabel('weight')
ax1.hist(df['A'])
ax2.hist(df['B'])
plt.tight_layout()
plt.show()