Develop Reference

Want to display only specific value in graph's x-axis , but its showing repeated values of columns of csv-file

I need to display only unique values on x-axis, but it is showing all the values in a specific column of the csv-file. Any suggestions please to fix this out?
df=pd.read_csv('//media//HOTEL MANAGEMENT.csv')
df.plot('Room_Type','Charges',color='g')
plt.show()

My assumption is that you are looking to plot the result of some aggregated data. e.g. Either:
The total charges per room type, or
The average charge per room type, or
The minimum/maximum charge per room type.
If so, you could so like:
df=pd.read_csv('//media//HOTEL MANAGEMENT.csv')
# And use any of the following:
df.groupby('Room_Type')['Charges'].sum().plot(color='g')
df.groupby('Room_Type')['Charges'].mean().plot(color='g')
df.groupby('Room_Type')['Charges'].min().plot(color='g')
df.groupby('Room_Type')['Charges'].max().plot(color='g')
Seeing that the x-axis may not necesarily be sequential, a comparative bar graph could be another way to plot.
df.groupby('Room_Type')['Charges'].mean().plot.bar(color=['r','g'])

Creating a line graph for a top X in a dataframe (Pandas)

I'm trying to make a line graph for my dataframe that has the names of 10 customers on the X axis and their amount of purchases they made on the Y axis.
I have over 100 customers in my data frame, so I created a new data frame that is grouped by customers and which shows the sum of their orders and I wish to only display the top 10 customers on my graph.
I have tried using
TopCustomers.nlargest(10, 'Company', keep='first')
But I run into the error nlargest() got multiple values for argument 'keep' and if I don't use keep, I get told it's a required argument.
TopCustomers is composed of TopCustomers = raw.groupby(raw['Company'])['Orders'].sum()
Sorting is not required at the moment, but it'd be good to know in advance.
On an additional Note: The list of customer's name is rather lengthy and, after playing with some dummy data, I see that the labels for the X axis are stacked on top of each other, is there a way to make it bigger so that all 10 are clearly visible? and maybe mark a dot where the X,Y meets?

we can do sort_values and tail
TopCustomers.sort_values().tail(10)

Plotting the proportion of each of one feature that contain a specific value of another feature

I have a data frame with multiple features, including two categorical: 'race' (5 unique values) and 'income' (2 unique values: <=$50k and >$50k)
I've figured out how to do a cross-tabulation table between the two.
However, I can't figure out a short way on how to create a table or bar graph that shows what percentage of each of the five races falls in the <=$50k income group
The code below gives me a table where the rows are the individual races; the counts for each of the two categories of income; and the total counts for each race. I can't figure out how to add another column on the right that simply takes the count for <=$50k, divides by the total, and then lists the proportion
ct_race_income=pd.crosstab(adult_df.race, adult_df.income, margins=True)
Here's a bunch of code where I do it the long way: calculating each proportion and then creating a new dataframe for the purposes of making a bar chart. However, I want to code all of this in many fewer lines
total_white=len(adult_df[adult_df.race=="White"])
total_black=len(adult_df[adult_df.race=="Black"])
total_hisp=len(adult_df[adult_df.race=="Hispanic"])
total_asian=len(adult_df[adult_df.race=="Asian"])
total_amer_indian=len(adult_df[adult_df.race=="Amer-Indian"])
prop_white=(len(adult_df_lowincome[adult_df_lowincome.race=="White"])/total_white)
prop_black=(len(adult_df_lowincome[adult_df_lowincome.race=="Black"])/total_black)
prop_hisp=(len(adult_df_lowincome[adult_df_lowincome.race=="Hispanic"])/total_hisp)
prop_asian=(len(adult_df_lowincome[adult_df_lowincome.race=="Asian"])/total_asian)
prop_amer_indian=(len(adult_df_lowincome[adult_df_lowincome.race=="Amer-Indian"])/total_amer_indian)
prop_lower_income=pd.DataFrame()
prop_lower_income['Race']=["White","Black","Hispanic", "Asian", "American Indian"]
prop_lower_income['Ratio']=[prop_white, prop_black, prop_hisp, prop_asian, prop_amer_indian]

producing a scatter plot from multi-level dataframe [pandas]

I have a big data frame, on which I've done a df.groupby(["event_type", "day"].count() and gotten the following multi-indexed df:
My aim is to produce a scatter plot that shows the number of occurrences of an event per day, sorted by event_type. So a scatter plot where the x axis is "day" and the y axis would be "id" from the above table (which is a count). But I don't know how to go about making it.
background: event_type is only 3 types. day is like 2 years of dates. "id" is id of things I'm tracking, but in the above .groupby() data frame, its actually the count of ids. I'd ideally like to get 3 separate lines plotted (one per event_type) of the id counts versus day of the year. Thanks!

I hope this will help:
a['date'] = pd.to_datetime(a['date'])
for name, group in a.groupby(['type','date']).count().groupby('type'):
plt.plot(group.reset_index().set_index('date')['v1'], marker=o, linestyle='', label=name)
plt.legend()
If you want normal plot instead of scatter, remove marker and linestyle arguments.
My DF looks like this:

How do I create a column chart with an aggregated column

I have two columns customer_id and revenue and I'm trying to figure out how to use matplotlib (or seaborn) to create a histogram/bar/column chart that has an aggregated column on the right. Everytime I change the range it just cuts off those values above my max range. Instead I want there to be a bin that is the count of instances above that max value.
For the example chart linked below, if I define my range as 0-1558, I want there be a column that counts the instances of all values $1558 and above and display that as a column.
Example Chart

Cap the values above the limit:
df[df['revenue']>limit] = limit
Now, plot the histogram.

Same concept as #DYZ, but my code ended up being:
df.ix[df.revenue > limit, 'revenue'] = limit