notebook.community

Edit and run

Pandas cheat sheet

This notebook has a bunch of useful reminders for how to work with Pandas.





In [ ]:



    


# Loading the data

%matplotlib inline

# makes plots look nicer
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'svg')

# actually imports matplotlib
import matplotlib.pyplot as plt

# imports pandas
import pandas

# creates a data frame called flint_data
flint_data = pandas.read_json("""[{"SampleID":1,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":0.344,"Pb Bottle 2 (ppb) - 45 secs flushing":0.226,"Pb Bottle 3 (ppb) - 2 mins flushing":0.145},{"SampleID":2,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":8.133,"Pb Bottle 2 (ppb) - 45 secs flushing":10.77,"Pb Bottle 3 (ppb) - 2 mins flushing":2.761},{"SampleID":4,"Zip Code":48504,"Ward":1,"Pb Bottle 1 (ppb) - First Draw":1.111,"Pb Bottle 2 (ppb) - 45 secs flushing":0.11,"Pb Bottle 3 (ppb) - 2 mins flushing":0.123},{"SampleID":5,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":8.007,"Pb Bottle 2 (ppb) - 45 secs flushing":7.446,"Pb Bottle 3 (ppb) - 2 mins flushing":3.384},{"SampleID":6,"Zip Code":48505,"Ward":3,"Pb Bottle 1 (ppb) - First Draw":1.951,"Pb Bottle 2 (ppb) - 45 secs flushing":0.048,"Pb Bottle 3 (ppb) - 2 mins flushing":0.035},{"SampleID":7,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":7.2,"Pb Bottle 2 (ppb) - 45 secs flushing":1.4,"Pb Bottle 3 (ppb) - 2 mins flushing":0.2},{"SampleID":8,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":40.63,"Pb Bottle 2 (ppb) - 45 secs flushing":9.726,"Pb Bottle 3 (ppb) - 2 mins flushing":6.132},{"SampleID":9,"Zip Code":48503,"Ward":5,"Pb Bottle 1 (ppb) - First Draw":1.1,"Pb Bottle 2 (ppb) - 45 secs flushing":2.5,"Pb Bottle 3 (ppb) - 2 mins flushing":0.1},{"SampleID":12,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":10.6,"Pb Bottle 2 (ppb) - 45 secs flushing":1.038,"Pb Bottle 3 (ppb) - 2 mins flushing":1.294},{"SampleID":13,"Zip Code":48505,"Ward":3,"Pb Bottle 1 (ppb) - First Draw":6.2,"Pb Bottle 2 (ppb) - 45 secs flushing":4.2,"Pb Bottle 3 (ppb) - 2 mins flushing":2.3},{"SampleID":15,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":4.358,"Pb Bottle 2 (ppb) - 45 secs flushing":0.822,"Pb Bottle 3 (ppb) - 2 mins flushing":0.147},{"SampleID":16,"Zip Code":48505,"Ward":5,"Pb Bottle 1 (ppb) - First Draw":24.37,"Pb Bottle 2 (ppb) - 45 secs flushing":8.796,"Pb Bottle 3 (ppb) - 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First Draw":4.401,"Pb Bottle 2 (ppb) - 45 secs flushing":2.111,"Pb Bottle 3 (ppb) - 2 mins flushing":1.572},{"SampleID":241,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":2.708,"Pb Bottle 2 (ppb) - 45 secs flushing":2.238,"Pb Bottle 3 (ppb) - 2 mins flushing":0.809},{"SampleID":242,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":34.13,"Pb Bottle 2 (ppb) - 45 secs flushing":6.002,"Pb Bottle 3 (ppb) - 2 mins flushing":1.71},{"SampleID":243,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":5.218,"Pb Bottle 2 (ppb) - 45 secs flushing":2.614,"Pb Bottle 3 (ppb) - 2 mins flushing":0.831},{"SampleID":244,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":15.73,"Pb Bottle 2 (ppb) - 45 secs flushing":13.95,"Pb Bottle 3 (ppb) - 2 mins flushing":3.584},{"SampleID":245,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":3.045,"Pb Bottle 2 (ppb) - 45 secs flushing":2.744,"Pb Bottle 3 (ppb) - 2 mins flushing":0.299},{"SampleID":246,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.1,"Pb Bottle 2 (ppb) - 45 secs flushing":0.5,"Pb Bottle 3 (ppb) - 2 mins flushing":0.3},{"SampleID":247,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.386,"Pb Bottle 2 (ppb) - 45 secs flushing":0.288,"Pb Bottle 3 (ppb) - 2 mins flushing":0.432},{"SampleID":248,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":0.915,"Pb Bottle 2 (ppb) - 45 secs flushing":0.354,"Pb Bottle 3 (ppb) - 2 mins flushing":0.306},{"SampleID":249,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":2.145,"Pb Bottle 2 (ppb) - 45 secs flushing":0.345,"Pb Bottle 3 (ppb) - 2 mins flushing":3.738},{"SampleID":250,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":4.056,"Pb Bottle 2 (ppb) - 45 secs flushing":0.547,"Pb Bottle 3 (ppb) - 2 mins flushing":0.378},{"SampleID":251,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.668,"Pb Bottle 2 (ppb) - 45 secs flushing":1.508,"Pb Bottle 3 (ppb) - 2 mins flushing":2.72},{"SampleID":252,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":7.575,"Pb Bottle 2 (ppb) - 45 secs flushing":1.362,"Pb Bottle 3 (ppb) - 2 mins flushing":1.094},{"SampleID":253,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":5.59,"Pb Bottle 2 (ppb) - 45 secs flushing":4.306,"Pb Bottle 3 (ppb) - 2 mins flushing":2.019},{"SampleID":254,"Zip Code":48503,"Ward":5,"Pb Bottle 1 (ppb) - First Draw":0.708,"Pb Bottle 2 (ppb) - 45 secs flushing":0.326,"Pb Bottle 3 (ppb) - 2 mins flushing":0.303},{"SampleID":255,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":1.701,"Pb Bottle 2 (ppb) - 45 secs flushing":4.397,"Pb Bottle 3 (ppb) - 2 mins flushing":1.287},{"SampleID":256,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":1.467,"Pb Bottle 2 (ppb) - 45 secs flushing":0.149,"Pb Bottle 3 (ppb) - 2 mins flushing":0.137},{"SampleID":258,"Zip Code":48504,"Ward":2,"Pb Bottle 1 (ppb) - First Draw":2.582,"Pb Bottle 2 (ppb) - 45 secs flushing":259.8,"Pb Bottle 3 (ppb) - 2 mins flushing":61.96},{"SampleID":259,"Zip Code":48505,"Ward":2,"Pb Bottle 1 (ppb) - First Draw":22.08,"Pb Bottle 2 (ppb) - 45 secs flushing":15.86,"Pb Bottle 3 (ppb) - 2 mins flushing":9.262},{"SampleID":260,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":16.51,"Pb Bottle 2 (ppb) - 45 secs flushing":2.024,"Pb Bottle 3 (ppb) - 2 mins flushing":7.068},{"SampleID":262,"Zip Code":48507,"Ward":8,"Pb Bottle 1 (ppb) - First Draw":56.26,"Pb Bottle 2 (ppb) - 45 secs flushing":4.692,"Pb Bottle 3 (ppb) - 2 mins flushing":1.243},{"SampleID":263,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":2.433,"Pb Bottle 2 (ppb) - 45 secs flushing":1.334,"Pb Bottle 3 (ppb) - 2 mins flushing":1.376},{"SampleID":264,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":0.5,"Pb Bottle 2 (ppb) - 45 secs flushing":0.2,"Pb Bottle 3 (ppb) - 2 mins flushing":0.5},{"SampleID":265,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":29.13,"Pb Bottle 2 (ppb) - 45 secs flushing":11.57,"Pb Bottle 3 (ppb) - 2 mins flushing":6.388},{"SampleID":266,"Zip Code":48505,"Ward":1,"Pb Bottle 1 (ppb) - First Draw":12.3,"Pb Bottle 2 (ppb) - 45 secs flushing":0.5,"Pb Bottle 3 (ppb) - 2 mins flushing":0.4},{"SampleID":267,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":3.445,"Pb Bottle 2 (ppb) - 45 secs flushing":0.29,"Pb Bottle 3 (ppb) - 2 mins flushing":0.167},{"SampleID":268,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":16.49,"Pb Bottle 2 (ppb) - 45 secs flushing":12.83,"Pb Bottle 3 (ppb) - 2 mins flushing":9.018},{"SampleID":269,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":3.365,"Pb Bottle 2 (ppb) - 45 secs flushing":2.45,"Pb Bottle 3 (ppb) - 2 mins flushing":1.675},{"SampleID":270,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.154,"Pb Bottle 2 (ppb) - 45 secs flushing":0.176,"Pb Bottle 3 (ppb) - 2 mins flushing":0.12},{"SampleID":271,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":13.53,"Pb Bottle 2 (ppb) - 45 secs flushing":21.91,"Pb Bottle 3 (ppb) - 2 mins flushing":4.675},{"SampleID":272,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":2.229,"Pb Bottle 2 (ppb) - 45 secs flushing":1.573,"Pb Bottle 3 (ppb) - 2 mins flushing":0.84},{"SampleID":273,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":28.91,"Pb Bottle 2 (ppb) - 45 secs flushing":5.471,"Pb Bottle 3 (ppb) - 2 mins flushing":3.056},{"SampleID":274,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":6.601,"Pb Bottle 2 (ppb) - 45 secs flushing":1.929,"Pb Bottle 3 (ppb) - 2 mins flushing":0.417},{"SampleID":275,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":0.948,"Pb Bottle 2 (ppb) - 45 secs flushing":0.27,"Pb Bottle 3 (ppb) - 2 mins flushing":0.207},{"SampleID":276,"Zip Code":48505,"Ward":3,"Pb Bottle 1 (ppb) - First Draw":3.484,"Pb Bottle 2 (ppb) - 45 secs flushing":0.434,"Pb Bottle 3 (ppb) - 2 mins flushing":0.306},{"SampleID":278,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.888,"Pb Bottle 2 (ppb) - 45 secs flushing":0.359,"Pb Bottle 3 (ppb) - 2 mins flushing":0.322},{"SampleID":279,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":13.95,"Pb Bottle 2 (ppb) - 45 secs flushing":12.2,"Pb Bottle 3 (ppb) - 2 mins flushing":8.251},{"SampleID":280,"Zip Code":48504,"Ward":6,"Pb Bottle 1 (ppb) - First Draw":6.27,"Pb Bottle 2 (ppb) - 45 secs flushing":4.036,"Pb Bottle 3 (ppb) - 2 mins flushing":1.182},{"SampleID":281,"Zip Code":48506,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":19.12,"Pb Bottle 2 (ppb) - 45 secs flushing":22.02,"Pb Bottle 3 (ppb) - 2 mins flushing":7.968},{"SampleID":282,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":1.633,"Pb Bottle 2 (ppb) - 45 secs flushing":0.465,"Pb Bottle 3 (ppb) - 2 mins flushing":0.238},{"SampleID":283,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":1.114,"Pb Bottle 2 (ppb) - 45 secs flushing":0.605,"Pb Bottle 3 (ppb) - 2 mins flushing":0.255},{"SampleID":284,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":3.9,"Pb Bottle 2 (ppb) - 45 secs flushing":0.558,"Pb Bottle 3 (ppb) - 2 mins flushing":0.504},{"SampleID":285,"Zip Code":48504,"Ward":1,"Pb Bottle 1 (ppb) - First Draw":3.521,"Pb Bottle 2 (ppb) - 45 secs flushing":0.45,"Pb Bottle 3 (ppb) - 2 mins flushing":0.321},{"SampleID":286,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":3.832,"Pb Bottle 2 (ppb) - 45 secs flushing":0.794,"Pb Bottle 3 (ppb) - 2 mins flushing":0.339},{"SampleID":287,"Zip Code":48505,"Ward":3,"Pb Bottle 1 (ppb) - First Draw":3.243,"Pb Bottle 2 (ppb) - 45 secs flushing":0.738,"Pb Bottle 3 (ppb) - 2 mins flushing":0.27},{"SampleID":289,"Zip Code":48503,"Ward":7,"Pb Bottle 1 (ppb) - First Draw":0.99,"Pb Bottle 2 (ppb) - 45 secs flushing":0.25,"Pb Bottle 3 (ppb) - 2 mins flushing":0.263},{"SampleID":290,"Zip Code":48507,"Ward":9,"Pb Bottle 1 (ppb) - First Draw":1.203,"Pb Bottle 2 (ppb) - 45 secs flushing":19.26,"Pb Bottle 3 (ppb) - 2 mins flushing":1.626},{"SampleID":291,"Zip Code":48506,"Ward":3,"Pb Bottle 1 (ppb) - First Draw":2.261,"Pb Bottle 2 (ppb) - 45 secs flushing":0.102,"Pb Bottle 3 (ppb) - 2 mins flushing":0.407},{"SampleID":292,"Zip Code":48503,"Ward":4,"Pb Bottle 1 (ppb) - 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First Draw":29.59,"Pb Bottle 2 (ppb) - 45 secs flushing":3.258,"Pb Bottle 3 (ppb) - 2 mins flushing":1.843},{"SampleID":300,"Zip Code":48505,"Ward":1,"Pb Bottle 1 (ppb) - First Draw":4.287,"Pb Bottle 2 (ppb) - 45 secs flushing":4.345,"Pb Bottle 3 (ppb) - 2 mins flushing":4.905}]""")

View the first few rows of data with .head()





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flint_data.head()

View the last few rows of data with .tail()





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flint_data.tail()

See the names of the columns in a dataset





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flint_data.columns

Select a single column of data





In [ ]:



    


# combining what we know from viewing just a few values
#  with .head()
flint_data['Pb Bottle 1 (ppb) - First Draw'].head()

Select multiple columns of data





In [ ]:



    


# note the double set of brackets [[ ]]
flint_data[['Zip Code', 'Ward']].head()



    











In [ ]:



    


# note the double set of brackets [[ ]]
flint_data[[
    'Pb Bottle 1 (ppb) - First Draw',
    'Zip Code',
    'Ward'
]].head()

Rename a single column of data with .rename_axis()





In [ ]:



    


flint_data = flint_data.rename_axis(
#   {'old_name': 'new_name'},   
    {'Zip Code': 'zip_code'}, 
    axis = 'columns'
)


flint_data = flint_data.rename_axis(
    {'Pb Bottle 1 (ppb) - First Draw': 'lead_in_first_bottle'},
    axis = 'columns'
)

flint_data.head() # we changed the column names!

Filter rows based on some condition





In [ ]:



    


#               We'll get back a column of True/False answers
#               and the column will be as long as 'lead_in_first_bottle'
exceeds_5_ppb = flint_data['lead_in_first_bottle'] > 5

#          Now we use that column to filter the rows of our data
#          and call head to look at the first few filtered rows
flint_data[exceeds_5_ppb].head()

# This is an equivalent way to filter, but it's a longer line to type
flint_data[flint_data['lead_in_first_bottle'] > 5].head()

Create a two-variable scatterplot with .plot.scatter()





In [ ]:



    


flint_data.plot.scatter(
    x = 'Pb Bottle 2 (ppb) - 45 secs flushing',
    y = 'Pb Bottle 3 (ppb) - 2 mins flushing'
)

Create a histogram with of a single column with .plot.hist()





In [ ]:



    


# select a single column, then add .plot.hist()
flint_data['Pb Bottle 3 (ppb) - 2 mins flushing'].plot.hist()

Create a line plot using .line.plot()





In [ ]:



    


flint_data.plot.line(
    x = 'SampleID',
    y = 'lead_in_first_bottle'
)

Content source: ComputationalModeling/spring-2017-danielak

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