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--- project:dncsonyc [2018/10/27 23:26] – [Dog Name Creativity Survey of New York City] dsievi
+++ project:dncsonyc [2018/10/28 15:42] (current) – [Dog Name Creativity Survey of New York City] birk
@@ Line 1: / Line 1: @@
 ===== Dog Name Creativity Survey of New York City =====
-(screenshots or sketches up here)
+{{:project:dncrsesult1.png?600|How does the creativity of given dog names related to the amount of culture found in the different boroughs of New York City?}}
-We started this project to see if art and cultural institutions in the environment have an impact on how creative people name their dogs. This was not possible with the date from Zurich because name-dataset does not contain information about the location and the dataset about the owners does not include the dognames. We choose to stick with our idea but used a different dataset: NYC Dog Licensing Dataset.
+We started this project to see if art and cultural institutions in the environment have an impact on the creativity of dognames. This was not possible with the date from Zurich because the name-dataset does not contain information about the location and the dataset about the owners does not include the dognames. We choose to stick with our idea but used a different dataset: NYC Dog Licensing Dataset.
-The creativity of name is measured by each letter in the name, which give points according to the letter frequency. The data for the cultural environment comes from Wikidata.
+The creativity of a name is measured by the frequency of each letter in the English language and gets +/- points according to the amount of dogs with the same name. The data for the cultural environment comes from Wikidata.
 After some data-cleaning with OpenRefine and failed attempts with OpenCalc we got the following code:
+<code>
+import string
+import pandas as pd
+numbers_ = {"e":1,"t":2,"a":3,"o":4,"n":5,"i":6,"s":7,"h":8,"r":9,"l":10,"d":11,"u":12,"c":13,"m":14,"w":15,"y":16,"f":17,"g":18,"p":19,"b":20,"v":21,"k":22,"j":23,"x":24,"q":25,"z":26}
+name_list = []
+def KreaWert(name_):
+    name_ = str(name_)
+    wert_ = 0
+    for letter in str.lower(name_):
+        temp_ = 0
+        if letter in string.ascii_lowercase :
+            temp_ += numbers_[letter]
+            wert_ += temp_
+    if name_ in H_:
+        wert_ = wert_* ((Hmax-H_[name_])/(Hmax-1)*5 + 0.2)
+    return round(wert_,1)
+df = pd.read_csv("Vds3.csv", sep = ";")
+df["AnimalName"] = df["AnimalName"].str.strip()
+H_ = df["AnimalName"].value_counts()
+Hmax = max(H_)
+Hmin = min(H_)
+df["KreaWert"] = df["AnimalName"].map(KreaWert)
+df.to_csv("namen2.csv")
+dftemp = df[["AnimalName", "KreaWert"]].drop_duplicates().set_index("AnimalName")
+dftemp.to_csv("dftemp.csv")
+df3 = pd.DataFrame()
+df3["amount"] = H_
+df3 = df3.join(dftemp, how="outer")
+df3.to_csv("data3.csv")
+df1 = round(df.groupby("Borough").mean(),2)
+df1.to_csv("data1.csv")
+df2 = round(df.groupby(["Borough","AnimalGender"]).mean(),2)
+df2.to_csv("data2.csv")
+</code>
 Visualisations were made with D3: https://d3js.org/