<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 NYC Restaurants
Read in data from csv, and check column names to keep in mind.
Step1:  Question 1
Step2:  Question 2
Step3:  Question 3
Step4:  Question 4
Step5:  Question 5
Step6:  Question 6
Step7:  Question 7
Step8:  Question 8
Step9:  Question 9
Step10:  Question 10
Step11:  Question 11
Step12:  Question 12
Step13:  Question 13
Step14:  Question 14
<ASSISTANT_TASK:>
Python Code:
restaurants = pd.read_csv("NYC_Restaurants.csv", dtype=unicode)
for index, item in enumerate(restaurants.columns.values):
    print index, item
Explanation: NYC Restaurants
Read in data from csv, and check column names to keep in mind.
End of explanation
#use .apply() method to combine the 4 columns to get the unique restaurant name
restaurants["RESTAURANT"] = restaurants[["DBA", "BUILDING", "STREET", "ZIPCODE"]].\
                                        apply(lambda x: "{} {} {} {}".format(x[0], x[1], x[2], x[3]), axis=1)
#incase that the RESTAURANT names contain spaces or symbols, strip off them
restaurants["RESTAURANT"] = restaurants["RESTAURANT"].map(lambda y: y.strip())
print restaurants["RESTAURANT"][:10]
Explanation: Question 1: Create a unique name for each restaurant
Select DBA, BUILDING, STREET and ZIPCODE columns as a dataframe
Apply apply() function on the selected dataframe, which takes in the series of the dataframe.
inside the apply() function, use placeholders to indicate that 4 series will be taken at the same time.
it is possible to select each column and concatenate them together, though looks not DRY.
End of explanation
print "There are", len(restaurants.drop_duplicates(subset="RESTAURANT")["RESTAURANT"].value_counts()), "restaurants in the data."
Explanation: Question 2: How many restaurants are included in the data?
Since each RESTAURANT appears appears only once in value_count() series, therefore applying len() will return the number of restaurants in the whole dataset.
End of explanation
num_chain = sum(restaurants.drop_duplicates(subset="RESTAURANT")["DBA"].value_counts()>=2)
print "There are", num_chain, "chain restaurants."
Explanation: Question 3: How many chains are there?
"Chains" are brands having at least 2 different RESTAURANT. After drop_duplicates(subset="RESTAURANT"), extractingvalue_count() on DBA will give how many RESTAURANT each DBA has. Converting each value into logical with evaluation value_count()&gt;=2 and then summing up the how series will give the number of True records, which is the number of chains.
End of explanation
chains = restaurants.drop_duplicates(subset="RESTAURANT")["DBA"].value_counts()[: num_chain].index.values
def chain(restaurant):
     return (restaurant in chains)
mask = restaurants["DBA"].map(chain)
restaurants[mask].drop_duplicates(subset="RESTAURANT")["DBA"].value_counts()[:20].plot(kind="bar")
Explanation: Question 4: Plot a bar graph of the top 20 most popular chains.
"Popularity" is here understood as number of RESAURANT of each DBA.
1. Extract the chain DBA 
2. Define a helper function chain to identify if a given DBA is a chain.
3. Use the helper function to make a mask to select the chain DBA.
4. Apply the mask to the whole dataframe, and drop duplicate RESTAURANT, the value_counts() will give the number of locations of each DBA
End of explanation
print "The percentage of chain restaurants is",
print "{:.2%}".format(sum(restaurants.drop_duplicates(subset="RESTAURANT")["DBA"].value_counts()>=2)/float(len(restaurants["RESTAURANT"].value_counts())))
Explanation: Question 5: What fraction of all restaurants are chains?
To calculate the faction of chains among all restaurants, we use an inline mask on DBA(True if is chain). Summing up True values gives the number of chains. It is divided by the total number of unique RESTAURANT to get the fraction.
End of explanation
def lower_case(X):
    return X.lower()
mask_1 = restaurants["DBA"].map(chain)
mask_2 = restaurants["BORO"].map(lower_case) != "missing"
restaurants[-mask_1].drop_duplicates(subset="RESTAURANT")[mask_2]["BORO"].value_counts().sort_values(ascending=False).plot(kind="bar")
Explanation: Question 6: Plot the number of non-chain restaurants in each boro.
In case "missing" is spelt differently, a helper function lower_case is defined to convert the string into lower case.
Use the chain helper function to make a mask selecting chains. Negative of this mask will return non-chains.
Use the lower_case function to select missing BORO.
Use the "negative" mask to select non-chains and remove duplicate RESTAURANT, and then remove missing BORO, value_counts() gives number of non-chains in each borough.
End of explanation
series_tmp_1 = restaurants[mask_2].drop_duplicates(subset="RESTAURANT")["BORO"].value_counts()
series_tmp_2 = restaurants[-mask_1][mask_2].drop_duplicates(subset="RESTAURANT")["BORO"].value_counts()
series_tmp_ratio = series_tmp_2/series_tmp_1
series_tmp_ratio.sort_values(ascending=False).plot(kind="bar")
print "The highest non-chain/total ratio is:", "{:0.2%} ({})".format(series_tmp_ratio.sort_values(ascending=False)[0],\
                                                                     series_tmp_ratio.sort_values(ascending=False).index.values[0])
if series_tmp_ratio.sort_values(ascending=False).index.values[0] !=\
restaurants[-mask_1].drop_duplicates(subset="RESTAURANT")[mask_2]["BORO"].value_counts().sort_values(ascending=False).index.values[0]:
    print "It is not the same borough."
else:
    print "It is the same borough."
Explanation: Question 7: Plot the fraction of non-chain restaurants in each boro.
The goal is to calculate the ratio of $\frac{N_{non-chain}}{N_{total}}$ within each borough.
This fraction can be done between two series-value_counts() of non-chains of BORO (not missing) and value_counts() of all unique RESTAURANT of BORO.
Depending on which borough has the highest ratio, a message will pop out to compare if it is the same with the borough with the most non-chains.
End of explanation
restaurants.drop_duplicates(subset="RESTAURANT")["CUISINE DESCRIPTION"].value_counts()\
                                                                .sort_values(ascending=False)[:20].plot(kind="bar")
Explanation: Question 8: Plot the popularity of cuisines.
Drop duplicate RESTAURANT and plot on the top 20 of sorted value_counts() of CUISINE DESCRIPTION.
End of explanation
non_clean_restaurants = restaurants[-restaurants["VIOLATION CODE"].isnull()]["RESTAURANT"].value_counts().index.values
def is_clean(restaurant, blacklist=non_clean_restaurants):
    return restaurant not in blacklist
mask_clean = restaurants["RESTAURANT"].map(is_clean)
restaurants[mask_clean]["CUISINE DESCRIPTION"].value_counts().sort_values(ascending=False)[:20].plot(kind="bar")
Explanation: Question 9: Plot the cuisines among restaurants which never got cited for violations.
Here we used a mask to sift out the restaurants whose VIOLATION CODE is missing.
End of explanation
top_cuisine_series = restaurants.drop_duplicates(subset=["RESTAURANT","CUISINE DESCRIPTION"])["CUISINE DESCRIPTION"].value_counts()
def is_top_cuisine(cuisine):
    return top_cuisine_series[cuisine]>=20
mask_3 = restaurants["VIOLATION CODE"].isnull()
mask_4 = restaurants["CUISINE DESCRIPTION"].map(is_top_cuisine)
series_tmp_3 = restaurants[mask_4][mask_3]["CUISINE DESCRIPTION"].value_counts()
series_tmp_4 = restaurants[mask_4]["CUISINE DESCRIPTION"].value_counts()
(series_tmp_3/series_tmp_4).sort_values(ascending=False)[:10].plot(kind="bar")
Explanation: Question 10: What cuisines tend to be the “cleanest”?
Make a series of all cuisines with 20 or more serving records in non-duplicate restaurants.
Define a helper function to determine if a given cuisine is in the series above.
Make a mask for the most served cuisines.
Apply that mask and the "non violation" mask in Q9 to produce a value_counts() series, containing the non-violation records for those cuisines.
Apply the newly defined mask to the whole DataFrame and produce another value_counts() containing how many inspections were done for the most served cuisines.
Divide the two series and get a new series of the format $cuisine:\ \frac{N_{non-violation}}{N_{total\ inspection}}$.
Plot the first 10 elements.
End of explanation
violation_boro_tab = pd.crosstab(
                        index=restaurants["VIOLATION DESCRIPTION"],
                        columns=restaurants[restaurants["BORO"]!="Missing"]["BORO"],
                        dropna=True
                    )
print "The most common violation in each borough is summarised below:"
violation_boro_tab.idxmax()
Explanation: Question 11: What are the most common violations in each borough?
Use crosstab to create a dataframe with VIOLATION DESCRIPTION as index, and BORO (without "Missing" boroughs) as columns. dropna is set True so NaN will not be recorded.
Every cell in the crosstab is the number of occurences of a violation in a certain borough. idxmax() method is applied to automatically retrieve the max occurence for each BORO.
End of explanation
violation_frequency_series = restaurants["VIOLATION DESCRIPTION"].value_counts()
violation_boro_norm_tab = violation_boro_tab.apply(lambda x: x.map(float)/violation_frequency_series, axis=0)
print "After normalization, the most common violation in each borough is summarised below:"
violation_boro_norm_tab.idxmax()
Explanation: Question 12: What are the most common violations per borough, after normalizing for the relative abundance of each violation?
Use apply() function to apply lambda x: x.map(float)/violation_frequency_series, axis=0 on each column of the above crosstab. The resulting series gives normalized violation frequency.
float() ensures the division returns fraction.
The denominator is a series of the value_counts() of all VIOLATION DESCRIPTION.
End of explanation
restaurants["AREA"] = restaurants["PHONE"].map(lambda x: x[:3])
print "There are",
print sum(restaurants.drop_duplicates(subset=["AREA", "ZIPCODE"])["AREA"].value_counts() == 1),
print "area codes corresponding to only 1 zipcode"
Explanation: Question 13: How many phone area codes correspond to a single zipcode?
Create a new column AREA to store the first 3 digits of PHONE, which is the area code.
Drop duplicate rows with the same combination of AREA and ZIPCODE.
By value_counts()==1 each AREA with a single ZIPCODE will return True.
Sum up True values to return the total number of such area codes.
End of explanation
restaurants["STREET TYPE"] = restaurants["STREET"].map(lambda s: s.split()[-1])
restaurants["STREET BASE"] = restaurants["STREET"].map(lambda s: " ".join(s.split()[:-1]))
restaurants["STREET BASE & ZIP"] = restaurants["STREET BASE"].map(lambda s: s+" ") + restaurants["ZIPCODE"]
new_dataframe = pd.concat(
    [restaurants["STREET BASE"], restaurants["STREET TYPE"], restaurants["STREET BASE & ZIP"]],
    axis=1
)
new_dataframe = new_dataframe[new_dataframe["STREET BASE"].map(lambda s: len(s)>0)].drop_duplicates()
merged_new_dataframe = pd.merge(
                            new_dataframe,
                            new_dataframe,
                            left_on="STREET BASE & ZIP",
                            right_on="STREET BASE & ZIP",
                            suffixes=[" 1", " 2"]
                                )
merged_new_dataframe = merged_new_dataframe[merged_new_dataframe["STREET TYPE 1"] != merged_new_dataframe["STREET TYPE 2"]]
street_name = pd.crosstab(
    index=merged_new_dataframe["STREET TYPE 1"],
    columns=merged_new_dataframe["STREET TYPE 2"],
    dropna=True
)
print "The most common alias for each of the following street type is listed"
street_name.idxmax()[
    ["AVE", "ST", "RD", "PL", "BOULEARD", "BOULEVARD"]
]
Explanation: Question 14: Find common misspellings of street names
map str.split() function on STREET to breakdown the string into a list of words, and take the last word as STREET TYPE.
Take the remaining words and join them together as STREET BASE.
Concatenate STREET BASE and STREET TYPE together as STREET BASE &amp; ZIP, spaced with empty space.
Create a new dataframe by concat the above 3 series. axis=1 meaning concatenating horizontally.
Remove duplicate records from the new dataframe, where STREET BASE is not empty.
Merge the new dataframe with itself to get cross-matched STREET TYPE.
Only keep rows where the two STREET TYPE are different.
Make another crosstab on the merged dataframe with one STREET TYPE as index and the other as columns.
In the new crosstab, the occurences of alternative STREET TYPE are recorded in cells, whose max occurence can be obtained with idxmax.
End of explanation
<END_TASK>