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In [2]:



    


import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
#don't do this, but it means the thing above: 
#from matplotlib import pyplot as plt



    











In [3]:



    


df = pd.read_excel("richpeople.xlsx")



    











In [4]:



    


recent = df[df['year'] == 2014]
recent = recent[['name','age','gender','selfmade','networthusbillion','countrycode','rank']]
recent = recent.sort_values(by='rank').head(8)
recent



    


















    
Out[4]:










  
    

      
      name
      age
      gender
      selfmade
      networthusbillion
      countrycode
      rank
    

  
  
    

      284
      Bill Gates
      58.0
      male
      self-made
      76.0
      USA
      1
    

    

      348
      Carlos Slim Helu
      74.0
      male
      self-made
      72.0
      MEX
      2
    

    

      124
      Amancio Ortega
      77.0
      male
      self-made
      64.0
      ESP
      3
    

    

      2491
      Warren Buffett
      83.0
      male
      self-made
      58.2
      USA
      4
    

    

      1377
      Larry Ellison
      69.0
      male
      self-made
      48.0
      USA
      5
    

    

      509
      David Koch
      73.0
      male
      inherited
      40.0
      USA
      6
    

    

      381
      Charles Koch
      78.0
      male
      inherited
      40.0
      USA
      6
    

    

      2185
      Sheldon Adelson
      80.0
      male
      self-made
      38.0
      USA
      8
    

  




















In [5]:



    


americans = recent[recent['countrycode'] == 'USA']
americans



    


















    
Out[5]:










  
    

      
      name
      age
      gender
      selfmade
      networthusbillion
      countrycode
      rank
    

  
  
    

      284
      Bill Gates
      58.0
      male
      self-made
      76.0
      USA
      1
    

    

      2491
      Warren Buffett
      83.0
      male
      self-made
      58.2
      USA
      4
    

    

      1377
      Larry Ellison
      69.0
      male
      self-made
      48.0
      USA
      5
    

    

      509
      David Koch
      73.0
      male
      inherited
      40.0
      USA
      6
    

    

      381
      Charles Koch
      78.0
      male
      inherited
      40.0
      USA
      6
    

    

      2185
      Sheldon Adelson
      80.0
      male
      self-made
      38.0
      USA
      8

Value counts vs groupby

groupby() whenever you want aggregate data - if you want totals of categories, or you want medians, ro you want to know the 75% percentile or the average or stuff values_counts() is for just if you want a count of the number in each category





In [8]:



    


recent['countrycode'].value_counts()



    


















    
Out[8]:








USA    6
ESP    1
MEX    1
Name: countrycode, dtype: int64

















In [9]:



    


recent['countrycode'].value_counts().plot()



    


















    
Out[9]:








<matplotlib.axes._subplots.AxesSubplot at 0x6241110>










    
























In [10]:



    


recent.groupby('countrycode')['networthusbillion'].sum()



    


















    
Out[10]:








countrycode
ESP     64.0
MEX     72.0
USA    300.2
Name: networthusbillion, dtype: float64

















In [11]:



    


recent.groupby('countrycode')['networthusbillion'].sum().plot()



    


















    
Out[11]:








<matplotlib.axes._subplots.AxesSubplot at 0x5f74530>










    
























In [13]:



    


recent = df[df['year'] == 2014]
recent = recent[['name', 'countrycode', 'networthusbillion']]
recent.head()



    


















    
Out[13]:










  
    

      
      name
      countrycode
      networthusbillion
    

  
  
    

      1
      A. Jerrold Perenchio
      USA
      2.6
    

    

      5
      Abdulla Al Futtaim
      ARE
      2.5
    

    

      6
      Abdulla bin Ahmad Al Ghurair
      ARE
      4.8
    

    

      8
      Abdullah Al Rajhi
      SAU
      2.4
    

    

      9
      Abdulsamad Rabiu
      NGA
      1.2
    

  




















In [14]:



    


country_gdp = [
   { 'country': 'USA', 'gdp': 17.418 },
   { 'country': 'DEU', 'gdp': 3.859 },
   { 'country': 'CHN', 'gdp': 10.380 },
   { 'country': 'RUS', 'gdp': 1.857 },
   { 'country': 'JPN', 'gdp': 4.616 },
   { 'country': 'BRA', 'gdp': 2.353 },
   { 'country': 'HKG', 'gdp': 0.289 },
   { 'country': 'FRA', 'gdp': 2.846 },
   { 'country': 'GBR', 'gdp': 2.945 },
   { 'country': 'IND', 'gdp': 2.049 }
]
country_gdp



    


















    
Out[14]:








[{'country': 'USA', 'gdp': 17.418},
 {'country': 'DEU', 'gdp': 3.859},
 {'country': 'CHN', 'gdp': 10.38},
 {'country': 'RUS', 'gdp': 1.857},
 {'country': 'JPN', 'gdp': 4.616},
 {'country': 'BRA', 'gdp': 2.353},
 {'country': 'HKG', 'gdp': 0.289},
 {'country': 'FRA', 'gdp': 2.846},
 {'country': 'GBR', 'gdp': 2.945},
 {'country': 'IND', 'gdp': 2.049}]

















In [15]:



    


gdp_df = pd.DataFrame(country_gdp)
gdp_df.head(2)



    


















    
Out[15]:










  
    

      
      country
      gdp
    

  
  
    

      0
      USA
      17.418
    

    

      1
      DEU
      3.859
    

  




















In [16]:



    


gdp_df['billions'] = gdp_df['gdp'] * 1000
gdp_df.head()



    


















    
Out[16]:










  
    

      
      country
      gdp
      billions
    

  
  
    

      0
      USA
      17.418
      17418.0
    

    

      1
      DEU
      3.859
      3859.0
    

    

      2
      CHN
      10.380
      10380.0
    

    

      3
      RUS
      1.857
      1857.0
    

    

      4
      JPN
      4.616
      4616.0
    

  




















In [17]:



    


recent.head(2)



    


















    
Out[17]:










  
    

      
      name
      countrycode
      networthusbillion
    

  
  
    

      1
      A. Jerrold Perenchio
      USA
      2.6
    

    

      5
      Abdulla Al Futtaim
      ARE
      2.5
    

  




















In [18]:



    


gdp_df.head(2)



    


















    
Out[18]:










  
    

      
      country
      gdp
      billions
    

  
  
    

      0
      USA
      17.418
      17418.0
    

    

      1
      DEU
      3.859
      3859.0
    

  




















In [19]:



    


#We are matchin up:
#countrycode from recent
#to
#country from gdp_df



    











In [22]:



    


merged = recent.merge(gdp_df, left_on="countrycode", right_on="country")
#on='country'
merged.head()



    


















    
Out[22]:










  
    

      
      name
      countrycode
      networthusbillion
      country
      gdp
      billions
    

  
  
    

      0
      A. Jerrold Perenchio
      USA
      2.6
      USA
      17.418
      17418.0
    

    

      1
      Abigail Johnson
      USA
      17.3
      USA
      17.418
      17418.0
    

    

      2
      Aerin Lauder Zinterhofer
      USA
      1.1
      USA
      17.418
      17418.0
    

    

      3
      Alan Gerry
      USA
      1.4
      USA
      17.418
      17418.0
    

    

      4
      Alec Gores
      USA
      2.0
      USA
      17.418
      17418.0
    

  




















In [23]:



    


len(gdp_df)



    


















    
Out[23]:








10

















In [24]:



    


recent['countrycode'].describe()



    


















    
Out[24]:








count     1653
unique      69
top        USA
freq       499
Name: countrycode, dtype: object

















In [26]:



    


merged[merged['countrycode'] == 'SAU'].head()



    


















    
Out[26]:










  
    

      
      name
      countrycode
      networthusbillion
      country
      gdp
      billions
    

  
  
  




















In [27]:



    


len(recent)



    


















    
Out[27]:








1653

















In [28]:



    


len(merged)



    


















    
Out[28]:








1130

















In [29]:



    


#a left join!
#will allow you to have empty non-matching values
#so we'll have our saudis back
left_merged = recent.merge(gdp_df, how="left", left_on="countrycode", right_on="country")
# on='country'
left_merged.head()



    


















    
Out[29]:










  
    

      
      name
      countrycode
      networthusbillion
      country
      gdp
      billions
    

  
  
    

      0
      A. Jerrold Perenchio
      USA
      2.6
      USA
      17.418
      17418.0
    

    

      1
      Abdulla Al Futtaim
      ARE
      2.5
      NaN
      NaN
      NaN
    

    

      2
      Abdulla bin Ahmad Al Ghurair
      ARE
      4.8
      NaN
      NaN
      NaN
    

    

      3
      Abdullah Al Rajhi
      SAU
      2.4
      NaN
      NaN
      NaN
    

    

      4
      Abdulsamad Rabiu
      NGA
      1.2
      NaN
      NaN
      NaN
    

  




















In [32]:



    


#left_merged['billion'] == 'NaN' does not work
left_merged['billions'].isnull()
#this will tell you if something is NaN or not
only_people_with_gdps = left_merged[left_merged['billions'].notnull()]
only_people_with_gdps.head()



    


















    
Out[32]:










  
    

      
      name
      countrycode
      networthusbillion
      country
      gdp
      billions
    

  
  
    

      0
      A. Jerrold Perenchio
      USA
      2.6
      USA
      17.418
      17418.0
    

    

      5
      Abigail Johnson
      USA
      17.3
      USA
      17.418
      17418.0
    

    

      6
      Abilio dos Santos Diniz
      BRA
      2.8
      BRA
      2.353
      2353.0
    

    

      8
      Adi Godrej
      IND
      3.5
      IND
      2.049
      2049.0
    

    

      9
      Aerin Lauder Zinterhofer
      USA
      1.1
      USA
      17.418
      17418.0
    

  




















In [34]:



    


#JOIN blah on blah.id = blah2.blah_id

nba= pd.read_csv("NBA-Census-10.14.2013.csv")



    


















    






---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-34-6050c80eb24e> in <module>()
      1 #JOIN blah on blah.id = blah2.blah_id
      2 
----> 3 nba= pd.read_csv("NBA-Census-10.14.2013.csv")

c:\users\kate\appdata\local\programs\python\python35-32\lib\site-packages\pandas\io\parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skip_footer, doublequote, delim_whitespace, as_recarray, compact_ints, use_unsigned, low_memory, buffer_lines, memory_map, float_precision)
    560                     skip_blank_lines=skip_blank_lines)
    561 
--> 562         return _read(filepath_or_buffer, kwds)
    563 
    564     parser_f.__name__ = name

c:\users\kate\appdata\local\programs\python\python35-32\lib\site-packages\pandas\io\parsers.py in _read(filepath_or_buffer, kwds)
    323         return parser
    324 
--> 325     return parser.read()
    326 
    327 _parser_defaults = {

c:\users\kate\appdata\local\programs\python\python35-32\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
    813                 raise ValueError('skip_footer not supported for iteration')
    814 
--> 815         ret = self._engine.read(nrows)
    816 
    817         if self.options.get('as_recarray'):

c:\users\kate\appdata\local\programs\python\python35-32\lib\site-packages\pandas\io\parsers.py in read(self, nrows)
   1312     def read(self, nrows=None):
   1313         try:
-> 1314             data = self._reader.read(nrows)
   1315         except StopIteration:
   1316             if self._first_chunk:

pandas\parser.pyx in pandas.parser.TextReader.read (pandas\parser.c:8620)()

pandas\parser.pyx in pandas.parser.TextReader._read_low_memory (pandas\parser.c:8876)()

pandas\parser.pyx in pandas.parser.TextReader._read_rows (pandas\parser.c:9893)()

pandas\parser.pyx in pandas.parser.TextReader._convert_column_data (pandas\parser.c:11286)()

pandas\parser.pyx in pandas.parser.TextReader._convert_tokens (pandas\parser.c:12192)()

pandas\parser.pyx in pandas.parser.TextReader._convert_with_dtype (pandas\parser.c:13625)()

pandas\parser.pyx in pandas.parser.TextReader._string_convert (pandas\parser.c:13871)()

pandas\parser.pyx in pandas.parser._string_box_utf8 (pandas\parser.c:19247)()

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xe9 in position 14: unexpected end of data
















In [ ]:

Content source: kbennion/foundations-hw

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