S/GMR Pandas&PythonPractice

# Start writing code here...

# import pandas as pd import pandas as pd # list of strings lst = ['Dog', 'Rat', 'Turtle'] # Calling DataFrame constructor on list df = pd.DataFrame(lst) print(df)

# This object is something called a dictionary petDictionary = {'Name':['Boo', 'Billy', 'Bortus'], 'Species': ['Dog', 'Rat', 'Tortois'], 'Age': [5, 2, 3]} print(petDictionary)

#Now we will make a dataframe from our dictioary petDataframe = pd.DataFrame(petDictionary) print(petDataframe)

#A dictionary has things called keys. Let's access them. for key in petDictionary: print(key)

#OK time to try and read in a data file ... it will be like one of the geo_info.txt files we use for S/GMR research import pandas as pd import matplotlib.pyplot as plt import numpy as np noPinsdata = pd.read_csv('non_Info07_forPandas.txt', usecols=range(25), delim_whitespace=True) #nondata is a dataframe object #Note: Above, delim_whitespace=True is needed b/c the csv file has different numbers of spaces between successive values in a row # The assignment of values into the columns will start to mess up if I don't use this. Thank you stackoverflow.com! # #Below are two ways to show the data. print() is less fancy; giving its name produces a cooler view of a dataframe object #print(noPinsdata) noPinsdata

%reset

#Lets now try to slice out columns that we desire phiColumn = noPinsdata["phi"] print("The column corresponding to Phi is \n", phiColumn)

rattler_counts = noPinsdata["rattler_count"] seeds = noPinsdata["seed"] #rattlersAndSeeds = np.column_stack((rattler_counts, seeds)) #The above seems to work, but here is another way to do a two-column numpy array rattlersAndseeds=np.array(rattler_counts,seeds) print("The numbers of ratters and the seed are", rattlersAndSeeds)

#let's practice accessing elements justRattlers = rattlersAndSeeds[:,0] justSeeds = rattlersAndSeeds[:,1] #print(justRattlers) print(justSeeds) plt.plot(justRattlers,justSeeds, 'ro')