new_contents: import seaborn as sns
  import matplotlib.pyplot as plt
  %matplotlib inline
  
  #------------------------------------------------------------------------------------
  # http://gree2.github.io/python/2015/05/05/python-seaborn-tutorial-controlling-figure-aesthetics
  # Reset all parameters to default
  sns.set()
  
  # STYLES
  # default is darkgrid, others include: whitegrid, dark, white
  sns.set_style('darkgrid')
  sns.set_style('whitegrid')
  sns.set_style('dark')
  sns.set_style('white')
  
  # SIZE
  # default is notebook, others include: paper, talk, poster
  sns.set_context('notebook')
  sns.set_context('paper')
  sns.set_context('talk')
  sns.set_context('poster')
  
  # REMOVE BORDER
  # only for white & tick styles
  # put at bottom of plot
  sns.despine() # remove top and right border
  sns.despine(left=True, bottom=True) # also remove left and bottom borders
  
  
  #------------------------------------------------------------------------------------
  # COLOR PALETTES
  # http://seaborn.pydata.org/tutorial/color_palettes.html
  # http://chrisalbon.com/python/seaborn_color_palettes.html
  sns.color_palette() # default palette
  sns.color_palette("deep", 10)
  
  # plot out color palettes
   sns.palplot(sns.color_palette("deep", 10))
  
  # see other palette types in links provided.
  
  
  #----------------------------- PLOTTING GRAPHS --------------------------------------
  #------------------------------------------------------------------------------------
    # GRAPH SETTINGS
  
  # add in x & y labels
  plt.xlabel('Agricultural Land %', fontsize=18) #change font size too!
  plt.ylabel('Protected Areas %')
  
  # change size, put this line in front. 2 is width, 6 is height
  # put at start of plot
  plt.figure(figsize=(2, 6))
  
  # rotate axis labels
  plt.xticks(rotation=90)
  #------------------------------------------------------------------------------------
    # MULTIPLE PLOTS
  
  # plot individual columns
  sub1=df[['VAR1','VAR2','VAR3']]
  sns.boxplot(data=sub1)
  
  # plot individual columns but by one levels of a categoical variable
  sub1=df[['CAT1','VAR1','VAR2','VAR3']]
  sub1.boxplot(by='CAT1')
  
  #------------------------------------------------------------------------------------
    # SINGLE AXIS GRAPHS
   
  # boxplot
    # vertical; if need horizontal change y to x
  sns.boxplot(y=df[df.columns[3]]) # dots within box not shown
  sns.stripplot(y=df[df.columns[i]], size=4, jitter=True, color="black") # boxes in plot shown
  
  # countplot
  sns.countplot(x=df[df.columns[1]], data=df)
  
  # distribution plot
  sns.distplot(x=df[df.columns[1]], data=df)
  
  #------------------------------------------------------------------------------------
    # DOUBLE AXES GRAPHS
   
  # regression plot & linear model plot
  sns.regplot(x=df.columns[7],y='PROTECTED_AREAS_T_M%', data=df)
  sns.lmplot(x=df.columns[7],y='PROTECTED_AREAS_T_M%', data=df)
    # add pearson's R
  import scipy.stats as ss
  ss.pearsonr(df3_layer['depth'],df3_layer['diameter'])
  
  # scatterplot
    # basically same as lmplot & regplot but add a fit_reg=False
  sns.lmplot(x=df.columns[7],y='PROTECTED_AREAS_T_M%', fit_reg=False, data=df)
   
  # barchart, using factorplot
  sns.factorplot(x='NUMBER_LAYERS',y='DEPTH_RIMFLOOR_TOPOG',kind='bar', data=df2)
  sns.factorplot(x='layers',y='depth',data=df3, kind='bar', ci=False) # remove confidence interval
  
  #------------------------------------------------------------------------------------
    # SUBPLOTS
  fig, ax = plt.subplots(ncols=3, nrows=2, figsize=(16, 20))
  
  sns.regplot(x=df[df.columns[1]], y='Protected Areas', data=df, ax=ax[0,0])
  sns.regplot(x=df[df.columns[2]], y='Protected Areas', data=df, ax=ax[0,1])
  sns.regplot(x=df[df.columns[3]], y='Protected Areas', data=df, ax=ax[0,2])
  sns.regplot(x=df[df.columns[4]], y='Protected Areas', data=df, ax=ax[1,0])
  sns.regplot(x=df[df.columns[5]], y='Protected Areas', data=df, ax=ax[1,1])
  sns.regplot(x=df[df.columns[6]], y='Protected Areas', data=df, ax=ax[1,2])
  
subject: Create seaborn.py