def ValidatePregnum(resp):
"""Validate pregnum in the respondent file.
resp: respondent DataFrame
"""
# read the pregnancy frame
preg = nsfg.ReadFemPreg()
# make the map from caseid to list of pregnancy indices
preg_map = nsfg.MakePregMap(preg)
# iterate through the preg_map
for caseid, indices in preg_map.items():
row = resp[resp.caseid == caseid]
pregnum = row.pregnum.values[0]
# check that pregnum from the respondent file equals
# the number of records in the pregnancy file
if len(indices) != pregnum:
print(caseid, len(indices), resp.pregnum[index])
return False
return True
def ValidatePregnum(resp):
"""Validate pregnum in the respondent file.
resp: respondent DataFrame
"""
# read the pregnancy frame
preg = nsfg.ReadFemPreg()
# make the map from caseid to list of pregnancy indices
preg_map = nsfg.MakePregMap(preg)
# iterate through the respondent pregnum series
for index, pregnum in resp.pregnum.items():
caseid = resp.caseid[index]
indices = preg_map[caseid]
# check that pregnum from the respondent file equals
# the number of records in the pregnancy file
if len(indices) != pregnum:
print(caseid, len(indices), pregnum)
return False
return True
def main(script):
"""Tests the functions in this module.
script: string script name
"""
# read and validate the respondent file
resp = ReadFemResp()
assert (len(resp) == 7643)
# read and validate the pregnancy file
preg = nsfg.ReadFemPreg()
print(preg.shape)
assert len(preg) == 13593
# validate that the pregnum column in `resp` matches the number
# of entries in `preg`
assert (ValidatePregnum(resp, preg))
PrintPregNums(preg)
print('%s: All tests passed.' % script)
def main():
thinkstats2.RandomSeed(17)
preg = nsfg.ReadFemPreg()
sf1 = PlotPregnancyData(preg)
# make the plots based on Cycle 6
resp6 = ReadFemResp2002()
sf2 = PlotMarriageData(resp6)
ResampleSurvival(resp6)
PlotRemainingLifetime(sf1, sf2)
# read Cycles 5 and 7
resp5 = ReadFemResp1995()
resp7 = ReadFemResp2010()
# plot resampled survival functions by decade
resps = [resp5, resp6, resp7]
PlotResampledByDecade(resps)
thinkplot.Save(root='survival4',
xlabel='age (years)',
ylabel='prob unmarried',
xlim=[13, 45],
ylim=[0, 1],
formats=FORMATS)
# plot resampled survival functions by decade, with predictions
PlotResampledByDecade(resps, predict_flag=True, omit=[5])
thinkplot.Save(root='survival5',
xlabel='age (years)',
ylabel='prob unmarried',
xlim=[13, 45],
ylim=[0, 1],
formats=FORMATS)
def myNSFG():
df = nsfg.ReadFemPreg()
# print(df.columns)
print(df.info())
df["agepreg"] /= 100
na_vals = [97, 98, 99]
df["birthwgt_lb"].replace(na_vals, np.nan, inplace=True)
df["birthwgt_oz"].replace(na_vals, np.nan, inplace=True)
df["totalwgt_lb"] = df["birthwgt_lb"] + df["birthwgt_oz"] / 16.0
# print(pregordr.describe())
# print(df["birthwgt_lb"].describe())
# print(df["birthwgt_lb"].isnull())
# print(df["birthwgt_lb"].value_counts(sort=False))
# print(df["outcome"].value_counts().sort_index())
# print(df["birthwgt_lb"].value_counts().sort_index())
d = defaultdict(list)
for index, caseid in df.caseid.iteritems():
d[caseid].append(index)
caseid = 10229
indices = d[caseid]
print(df.outcome[indices].values)
"""Use the dict returned by MakePregMap to validate
resp: dataframe with nsfg respondents
preg: dataframe with nsfg pregnancies
"""
dict_preg = nsfg.MakePregMap(preg)
validatecases = []
for key, value in dict_preg.items():
preg_val = resp.loc[resp.caseid == key,
'pregnum'].values[0] - len(value)
if preg_val != 0:
validatecases.append(key)
print(len(validatecases))
if __name__ == '__main__':
main(*sys.argv)
resp = nsfg.ReadFemResp()
preg = nsfg.ReadFemPreg()
#part one
#print(resp.pregnum.value_counts().sort_index())
#part two first attempt
#CrossValidatePregnum(resp, preg)
#part three with dict
CrossValPythonically(resp, preg)
# Author: Matt Xiao # Data frame from ThinkStats import nsfg import thinkstats2 import thinkplot df = nsfg.ReadFemPreg() hist = thinkstats2.Hist([1, 2, 2, 3, 5]) thinkplot.Hist(hist) thinkplot.Show(xlabel='value', ylabel='frequency')
def main(script):
preg = nsfg.ReadFemPreg() # DataFrame
resp = nsfg.ReadFemResp() # DataFrame
preg_by_caseid = MakePregMap(preg) # dictionary
total_pregnancies_by_caseid = {}
for key, preg_list in preg_by_caseid.items():
total_preg = 0
for p in preg_list:
total_preg += 1
total_pregnancies_by_caseid[key] = total_preg
print(len(total_pregnancies_by_caseid))
print(len(resp))
# for k, v in total_pregnancies_by_caseid.items():
# print(k,v)
# # iterate through the respondent pregnum series
# for index, pregnum in resp.pregnum.iteritems():
# caseid = resp.caseid[index]
# indices = total_pregnancies_by_caseid[caseid]
# # check that pregnum from the respondent file equals
# # the number of records in the pregnancy file
# if indices != pregnum:
# print(caseid, indices, pregnum)
# preg_pregnum = pd.DataFrame([total_pregnancies_by_caseid], columns=['caseid', 'pregnum'])
# result = ValidatePregnum(resp, preg_pregnum)
# print(result)
# df = ReadFemResp()
# print(df.pregnum.head())
# print(df.pregnum.value_counts().sort_index())
# bins = [0,1,2,3,4,5,6,100]
# print(pd.cut(df.pregnum, bins).value_counts().sort_index())
# preg = nsfg.ReadFemPreg()
# resp = nsfg.ReadFemResp()
# print(ValidatePregnum(resp, preg))
# print(preg.head())
# pregnum_map = nsfg.MakePregMap(preg)
# # pprint(pregnum_map)
# print(len(pregnum_map))
# print(len(resp))
# for key, value in pregnum_map.items():
# pass
# print(key, len(value))
# print(type(resp.pregnum[key]))
# if resp.pregnum[key] == len(value):
# print("MATCH")
# elif resp.pregnum[key] != len(value):
# print("NO MATCH")
# else:
# print("ERROR")
# print(pregnum_map)
# print(resp.pregnum)
# caseid = 12556
# pregnum_map = nsfg.MakePregMap(preg)
# indices = pregnum_map[caseid]
# # resp.pregnum[indices].values
# result = preg.pregnum
# print(result)
# print(resp.head())
# print(result)
"""Tests the functions in this module.
def MakeDataFrames():
preg = nsfg.ReadFemPreg()
live = preg[preg.outcome == 1]
first = live[live.birthord == 1]
other = live[live.birthord != 1]
return (live, first, other)
def main():
df = ReadData()
print(df)
df = nsfg.ReadFemPreg()
print(df.shape)
# coding: utf-8
# In[1]:
from __future__ import print_function, division
import nsfg
# In[2]:
nsfg.ReadFemPreg()
pres = nsfg.ReadFemPreg()
# In[3]:
pres
# In[4]:
pres.head(20)
# In[5]:
pres.tail(30)
# In[6]:
print("There are", len(pres.index), "rows and", len(pres.columns), "columns.")
# In[7]:
#import pandas as pd
import random
import scipy.stats
import sys
directory = '/Users/warren/Data_Science/metis/github/prework/dsp/ThinkStats2/code/'
sys.path.append(directory)
import nsfg
import thinkstats2
import thinkplot
# Q1
# Would like to have a concise/readable way to perform these data manipulations
# without the need to define the intermediate "live" table, as done here.
preg = nsfg.ReadFemPreg(dct_file=directory + '2002FemPreg.dct',
dat_file=directory + '2002FemPreg.dat.gz')
live = preg[preg.outcome == 1]
firsts = live[live.birthord == 1]['totalwgt_lb']
others = live[live.birthord != 1]['totalwgt_lb']
# This was defined in the book, but it's not in nsfg, so I copied and pasted the
# code here. I can understand its contents.
def CohenEffectSize(group1, group2):
diff = group1.mean() - group2.mean()
var1 = group1.var()
var2 = group2.var()
n1, n2 = len(group1), len(group2)
def MakeDataframes():
preg = nsfg.ReadFemPreg();
live = preg[preg.outcome == 1];
first = live[live.birthord == 1] ;
other = live[live.birthord != 1] ;
return (live,first,other) ;
def ValueCounts(resp):
preg = nsfg.ReadFemPreg()
preg_map = nsfg.MakePregMap(preg)
preg.pregnum_counts().sort_index()
def validate():
resp = ReadFemResp()
pregs = sum(resp.pregnum.value_counts())
preg = nsfg.ReadFemPreg()
def main(script):
preg = nsfg.ReadFemPreg()
print(preg.pregnum.value_counts().sort_index())
pregMap = nsfg.MakePregMap(preg)