def create_task():
df = clean_df(request.json)
df_customer = df >> mutate(name=X.FullNameBilling.str.upper()) >> group_by(
X.name) >> summarize(contact=X.PhoneBilling.head(1),
email=X.EmailBilling.head(1),
address=X.Address2Billing.head(1),
num_items_purchased=(X.name).count())
jsondf = df_customer.to_json(orient='records')
return (jsondf)
def create_task():
df = clean_df(request.json)
df_customer = df >> mutate(name=X.FullNameBilling.str.upper()) >> group_by(X.name) >> summarize(contact = X.PhoneBilling.head(1),
email = X.EmailBilling.head(1),
address = X.Address2Billing.head(1),
num_items_purchased = (X.name).count()
)
jsondf = df_customer.to_json(orient='records')
return (jsondf);
def create_task2(): df = clean_df(request.json) print(df["Category"]) df['supplier'] = df['Category'].apply(lambda x: supp(x)) df = DplyFrame(df) >> group_by(X.supplier) >> summarize(max1 = most_common( X.Name ) ) print(df) # df_fav = df >> mutate(new = supp(X.Category)) jsondf = df.to_json(orient='records') return (jsondf);
def create_task2():
df = clean_df(request.json)
print(df["Category"])
df['supplier'] = df['Category'].apply(lambda x: supp(x))
df = DplyFrame(df) >> group_by(
X.supplier) >> summarize(max1=most_common(X.Name))
print(df)
# df_fav = df >> mutate(new = supp(X.Category))
jsondf = df.to_json(orient='records')
return (jsondf)
# data=iris %>%
# select(Petal.Length, Petal.Width, Sepal.Length, Sepal.Width, Species)
iris >> dp.select(X.Species) >> dp.head()
iris[['Species', 'PetalLength']]
iris.drop('SepalLength', axis=1) #quitar esa columna
iris.drop(5, axis=0) #quitar la sexta fila
# data=iris %>%
# filter(Petal.Length>1 & Petal.Length<100)
iris >> dp.sift(X.PetalLength>5)
iris[(iris['PetalLength']>5) & (iris['PetalLength']<6)]
# data=iris %>%
# dplyr::group_by(Species) %>%
# summarise(media=mean(Petal.Length))
iris >> dp.group_by(X.Species) >> dp.summarize(media=X.PetalLength.mean())
iris.groupby(['Species'])['PetalLength'].agg(['mean', 'sum', 'count'])
iris.groupby(['Species'])['PetalLength'].agg({'var1':'mean', 'var2':'sum', 'var3':'count'})
iris.groupby(['Species'])['PetalLength'].agg({'var1':['mean', 'sum']})
aggregations = {
'dsuma':'sum',
}
import math
iris.groupby(['Species'])['PetalLength'].agg({'dsuma':'sum', 'otro': lambda x: math.sqrt(x.mean()) - 1})
# data=iris %>%
# mutate(total=Sepal.Length+Petal.Length, otro=ifelse(Petal.Length>2, "grande", "pequeño"))
iris >> dp.mutate(redondeado=X.PetalLength.round(), redondeado2=X.SepalLength.round())
iris.assign(redondeado = lambda x: x.PetalLength.round(), redondeado2 = lambda x: x.SepalLength.round())
def main(argv):
yURL = None
outdir = None
maxFrames = 500
yURL = input("Enter the youtube url:")
outdir = input("Enter the output directory:")
maxFrames = int(input("Enter the maximum number of frames to check:"))
faceDet = cv2.CascadeClassifier(
"haarcascade/haarcascade_frontalface_default.xml")
faceDet2 = cv2.CascadeClassifier(
"haarcascade/haarcascade_frontalface_alt2.xml")
faceDet3 = cv2.CascadeClassifier(
"haarcascade/haarcascade_frontalface_alt.xml")
faceDet4 = cv2.CascadeClassifier(
"haarcascade/haarcascade_frontalface_alt_tree.xml")
#
pdata, pframes, pfacedims = getNewInstances(yURL,
faceDet,
faceDet2,
faceDet3,
faceDet4,
maxCount=maxFrames)
#
headers = dict()
headers['Ocp-Apim-Subscription-Key'] = ms_key1
headers['Content-Type'] = 'application/octet-stream'
#
resultsDf = pd.DataFrame()
frameId = 0
for image in pframes:
print("posting frame %d of %d" % (frameId, len(pframes)))
#sending the frame image to MS cognitive services
resultMS = processRequest(image, headers)
#isinstance == type()
if isinstance(resultMS, list):
for result in resultMS:
if isinstance(result, dict):
resFrameList = []
for res in result['scores'].items():
resFrameList.append(
(frameId, res[0], res[1],
result["faceRectangle"]['left'],
result["faceRectangle"]['top'],
result["faceRectangle"]['width'],
result["faceRectangle"]['height']))
appendDf = pd.DataFrame(resFrameList,
columns=[
"frameId", "emotionLabel",
"conf", "faceleft",
"facetop", "faceW", "faceH"
])
resultsDf = resultsDf.append(appendDf)
time.sleep(2)
frameId += 1
#
# print(resultsDf)
#we append all the data to the dataframe
#http://bluescreen.club/2017/06/18/import-pandas-as-pd/
#then we convert the dataframe to a Dplyframe object which allows us to do higher level data analytics
#for this one, we will select out the top most ranking face frames for each of the emotions
#microsoft provides us with around 8 emotions
#so we sort out 8 faces for 8 emotions and then save them accordingly
dfFaces = DplyFrame(resultsDf)
# print(dfFaces)
topFaces = (
dfFaces >> group_by(X.emotionLabel) >> sift(X.conf == X.conf.max()) >>
sift(X.frameId == X.frameId.min()) >> ungroup() >> group_by(
X.frameId) >> sift(X.conf == X.conf.max()) >> ungroup() >> arrange(
X.emotionLabel))
topFaces = topFaces.drop_duplicates()
#print(topFaces)
i = 0
for index, row in topFaces.iterrows():
print("saving emotion frame %d of %d" % (i, len(topFaces.index)))
#
emotion = row["emotionLabel"]
confid = int(row["conf"] * 100)
image = pframes[int(row["frameId"])]
faceL = row["faceleft"]
faceT = row["facetop"]
faceW = row["faceW"]
faceH = row["faceH"]
#save cropped face
imageW = image[faceT:faceT + faceH, faceL:faceL + faceW]
cv2.imwrite(
os.path.expanduser("%s/Cropped_%s.jpg" % (outdir, emotion)),
imageW)
#if you wish to put a rectangle on the faces then uncomment below
#
# cv2.rectangle( image,(faceL,faceT),
# (faceL+faceW, faceT + faceH),
# color = (255,0,0), thickness = 5 )
# cv2.putText( image, emotion, (faceL,faceT-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255,0,0), 1 )
#
cv2.imwrite(os.path.expanduser("%s/%s.jpg" % (outdir, emotion)), image)
i += 1
diamonds >> drop(columns_from(X.price)) >> head(2)
# mixing techniques to select first 2 cols, 'category' col and last 2 cols
diamonds >> select(columns_to(1, inclusive=True), 'depth', columns_from(-2)) >> head(2)
'''
starts_with(prefix): find columns that start with a string prefix.
ends_with(suffix): find columns that end with a string suffix.
contains(substr): find columns that contain a substring in their name.
everything(): all columns.
columns_between(start_col, end_col, inclusive=True): find columns between a specified start and end column. The inclusive boolean keyword argument indicates whether the end column should be included or not.
columns_to(end_col, inclusive=True): get columns up to a specified end column. The inclusive argument indicates whether the ending column should be included or not.
columns_from(start_col): get the columns starting at a specified column.
'''
# Subsetting and filtering
diamonds >> row_slice([10,15]) # diamonds.iloc[[10,15]]
diamonds >> row_slice(list(range(5,10))) # diamonds.iloc[10:15]
diamonds >> group_by('cut') >> row_slice(5)
diamonds >> sample(frac=0.0001, replace=False) # % of df rows returned
diamonds >> sample(n=3, replace=True) # number of rows returned
diamonds >> distinct(X.color) # Selection of unique rows
diamonds >> mask(X.cut == 'Ideal') >> head(4) # Filtering rows with logical criteria
# mask() can also be called using the alias filter_by()
diamonds >> filter_by(X.cut == 'Ideal', X.color == 'E', X.table < 55, X.price < 500)
# pull simply retrieves a column and returns it as a pandas series, in case you only care about one particular column at the end of your pipeline
(diamonds
>> filter_by(X.cut == 'Ideal', X.color == 'E', X.table < 55, X.price < 500)
>> pull('carat'))
# DataFrame transformation
"""to filter the bigrams only"""
bigr = output[output['word'].str.contains("_")]
"""FROM THIS PART, 2 STRATEGIES, SAVE THE OUTPUT AND CONTINUE W R OR GO AHEAD W PYTHON"""
"""5 plotting"""
"""5 1 aggregating for plotting"""
from dplython import (DplyFrame, X, diamonds, select, sift, sample_n,
sample_frac, head, arrange, mutate, group_by, summarize, DelayFunction)
dfr = DplyFrame(output)
dfr = (dfr >>
group_by(X.word, X.source) >>
summarize(tot=X.count.sum()))
dff = (dfr >>select(X.word, X.tot ))
"""5.2 wordcloud"""
"""turns the word freq to dict"""
d = {}
for a, x in dff.values:
d[a] = x
wordcloud = WordCloud(width = 1000, height = 1000,
background_color ='white',
min_font_size =15, max_font_size=120).generate_from_frequencies(frequencies=d)
plt.figure(figsize = (8, 8), facecolor = None)
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad = 0)
def czMatchmaker(data, Q, precursor_fasta):
data = pd.read_csv(
"/Users/matteo/Documents/czMatchmaker/data/examplaryData.csv")
data = DplyFrame(data)
precursors = data >> \
sift( X.tag == 'precursor' ) >> \
select( X.active, X.neutral, X.estimates)
fragments = data >> sift( X.tag != 'precursor' ) >> \
group_by( X.tag, X.active, X.broken_bond ) >> \
summarize( estimates = X.estimates.sum() )
I_on_fragments = {}
optiminfos = {}
for break_point, data in fragments.groupby('broken_bond'):
pairing, optiminfo = collect_fragments(data, Q)
I_on_fragments[break_point] = pairing
optiminfos[break_point] = optiminfo
cations_fragmented_I = sum(
sum(I_on_fragments[bP][p] for p in I_on_fragments[bP])
for bP in I_on_fragments)
I_no_reactions = precursors >> \
sift( X.active==Q, X.neutral == 0) >> \
select( X.estimates )
I_no_reactions = I_no_reactions.values.flatten()[0]
prec_ETnoD_PTR_I = precursors >> \
sift( X.active != Q ) >> \
rename( ETnoD = X.neutral, I = X.estimates ) >> \
mutate( PTR = Q - X.ETnoD - X.active ) >> \
select( X.ETnoD, X.PTR, X.I )
I_prec_no_frag = prec_ETnoD_PTR_I >> \
summarize( I = X.I.sum() )
I_prec_no_frag = I_prec_no_frag.values.flatten()[0]
precursorNoReactions = precursors >> \
sift( X.active == Q ) >> \
select( X.estimates )
prec_ETnoD_PTR_I = prec_ETnoD_PTR_I >> mutate(
I_PTR = crossprod(X.PTR, X.I), \
I_ETnoD = crossprod(X.ETnoD, X.I) ) >> \
summarize( I_PTR = X.I_PTR.sum(), I_ETnoD = X.I_ETnoD.sum() )
I_PTR_no_frag, I_ETnoD_no_frag = prec_ETnoD_PTR_I.values.flatten()
prob_PTR = I_PTR_no_frag / (I_PTR_no_frag + I_ETnoD_no_frag)
prob_ETnoD = 1. - prob_PTR
I_frags = dict(
(bP, sum(I_on_fragments[bP][pairing]
for pairing in I_on_fragments[bP])) for bP in I_on_fragments)
I_frag_total = sum(I_frags[bP] for bP in I_frags)
prob_frag = Counter(
dict((int(bP), I_frags[bP] / I_frag_total) for bP in I_frags))
prob_frag = [prob_frag[i] for i in range(len(precursor_fasta))]
I_frags_PTRETnoD_total = sum(
(Q - 1 - sum(q for cz, q in pairing)) * I_on_fragments[bP][pairing]
for bP in I_on_fragments for pairing in I_on_fragments[bP])
anion_meets_cation = I_frags_PTRETnoD_total + I_PTR_no_frag + I_ETnoD_no_frag
prob_fragmentation = I_frags_PTRETnoD_total / anion_meets_cation
prob_no_fragmentation = 1 - prob_fragmentation
prob_no_reaction = I_no_reactions / (I_no_reactions + I_frag_total +
I_prec_no_frag)
prob_reaction = 1. - prob_no_reaction
res = {}
res['reaction'] = (prob_reaction, prob_no_reaction)
res['fragmentation'] = (prob_fragmentation, prob_no_fragmentation)
res['fragmentation_amino_acids'] = tuple(prob_frag)
return res
import altair as alt
firsts = pd.read_csv(
'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-06-09/firsts.csv'
)
firsts.to_csv('/Users/vivekparashar/Downloads/firsts.csv')
# Create/Convert a pandas dataframe to dplython df
firsts = DplyFrame(firsts)
firsts.columns
firsts.gender.unique()
firsts.category.unique()
# firsts df summary by category
t1 = (firsts >> mutate(year_grp=((X.year / 10).round()) * 10) >> group_by(
X.year_grp, X.category) >> summarize(nrows=X.accomplishment.count()))
c1 = alt.Chart(t1).mark_circle().encode(x='year_grp:O',
y='category:O',
size='nrows:Q')
c3 = alt.Chart(t1).mark_bar().encode(x='year_grp', y='nrows', color='category')
# firsts df summary by gender
t2 = (firsts >> mutate(year_grp=((X.year / 10).round()) * 10) >> group_by(
X.year_grp, X.gender) >> summarize(nrows=X.accomplishment.count()))
c2 = alt.Chart(t2).mark_circle().encode(x='year_grp:O',
y='gender:O',
size='nrows:Q')
chart = alt.vconcat(c2, c1, c3)
chart.save(
'/Users/vivekparashar/OneDrive/OneDrive-GitHub/Challenges-and-Competitions/TidyTuesday/Data/2020-11-17/chart.png',
import pandas
from dplython import (DplyFrame, X, diamonds, select, sift, sample_n,
sample_frac, head, arrange, mutate, group_by, summarize,
DelayFunction)
diamonds >> head(5)
diamonds >> select(X.carat, X.cut, X.price) >> head(5)
d = (diamonds >> sift(X.carat > 4) >> select(X.carat, X.cut, X.depth, X.price)
>> head(2))
(diamonds >> mutate(carat_bin=X.carat.round()) >> group_by(X.cut, X.carat_bin)
>> summarize(avg_price=X.price.mean()))
test = df['deaths'] < 0
less_than_zero = df[test]
print(less_than_zero.shape)
print(less_than_zero.head())
test
#df['deaths_fixed'] = df['deaths_new'].apply(lambda x: 'True' if x <= 0 else 'False')
def load_data(input_dir, crsrd_id):
cctv_log = pd.read_csv(input_dir + "/ORT_CCTV_5MIN_LOG.csv")
cctv_mst = pd.read_csv(input_dir + "/ORT_CCTV_MST.csv")
cctv_log['DATE'] = pd.DataFrame(pd.DatetimeIndex(cctv_log['REG_DT']).date)
cctv_log['HOUR'] = pd.DataFrame(pd.DatetimeIndex(cctv_log['REG_DT']).hour)
cctv_log['MINUTE'] = (
pd.DataFrame(pd.DatetimeIndex(cctv_log['REG_DT']).minute) // 30) * 30
cctv_log['temp_DAY'] = pd.to_datetime(cctv_log['DATE']).dt.dayofweek
cctv_log.loc[cctv_log['temp_DAY'] < 5, 'DAY'] = int(0) #mon - fri
cctv_log.loc[cctv_log['temp_DAY'] == 5, 'DAY'] = int(1) #sat
cctv_log.loc[cctv_log['temp_DAY'] == 6, 'DAY'] = int(2) #sun
df0 = DplyFrame(cctv_log) >> group_by(
X.DATE, X.DAY, X.HOUR, X.MINUTE, X.CCTV_ID) >> summarize(
GO_TRF=X.GO_BIKE.sum() + X.GO_CAR.sum() + X.GO_SUV.sum() +
X.GO_VAN.sum() + X.GO_TRUCK.sum() + X.GO_BUS.sum() +
X.RIGHT_BIKE.sum() + X.RIGHT_CAR.sum() + X.RIGHT_SUV.sum() +
X.RIGHT_VAN.sum() + X.RIGHT_TRUCK.sum() + X.RIGHT_BUS.sum(),
LEFT_TRF=X.LEFT_BIKE.sum() + X.LEFT_CAR.sum() + X.LEFT_SUV.sum() +
X.LEFT_VAN.sum() + X.LEFT_TRUCK.sum() + X.LEFT_BUS.sum())
# Extract records of selected crossroad
cctv_mst = DplyFrame(cctv_mst) >> sift(X.CRSRD_ID == crsrd_id) >> select(
X.CRSRD_ID, X.CCTV_ID)
df0 = pd.merge(df0, cctv_mst, how="inner", on="CCTV_ID")
df0 = df0.sort_values(['DATE', 'HOUR', 'MINUTE', 'CCTV_ID'])
# Time frame from existing dataset
tf = DplyFrame(
df0.drop_duplicates(
['DATE', 'DAY', 'HOUR', 'MINUTE'], keep='last')) >> select(
X.DATE, X.DAY, X.HOUR, X.MINUTE)
# Process the datastructure into pivot
cctv_list = sorted(cctv_mst['CCTV_ID'].unique())
df1 = tf
for cctv in cctv_list:
a = df0 >> sift(X.CCTV_ID == cctv) >> select(
X.DATE, X.DAY, X.HOUR, X.MINUTE, X.GO_TRF, X.LEFT_TRF)
df1 = pd.merge(df1,
a,
how='left',
on=['DATE', 'DAY', 'HOUR', 'MINUTE'],
suffixes=('', '_' + str(cctv)))
df1 = df1.set_index(['DATE', 'DAY', 'HOUR', 'MINUTE'])
df1 = df1.fillna(df1.rolling(window=24, min_periods=1, center=True).mean())
df1 = df1.fillna(0)
df1 = df1.reset_index()
df1['TOTAL_TRF'] = DplyFrame(df1.iloc[:, 4:3 + len(cctv_list) * 2].sum(
axis=1, skipna=True))
df1 = df1 >> sift(X.TOTAL_TRF > 0)
print(df1)
# Name the cctv id and direction - for tod_traffic_analyzer
cols = [cctv + '_GO_RATE' for cctv in cctv_list]
cols.extend([cctv + '_LEFT_RATE' for cctv in cctv_list])
cols = sorted(cols)
cols = ['TOD'] + cols + ['TOTAL_TRF']
return df1, cols
def main(argv):
ytURL = None
outdir = None
maxFrames = 500
try:
opts, args = getopt.getopt(argv, "hy:o:m:",
["yturl=", "odir=", "maxframes="])
except getopt.GetoptError:
print 'Error: shellScript.py -y <yturl> -o <odir> -m <maxframes>'
sys.exit(2)
#print opts
for opt, arg in opts:
if opt == '-h':
print 'help: shellScript.py -y <yturl> -o <odir> -m <maxframes>'
sys.exit()
elif opt in ("-y", "--yturl"):
print("--yturl={}".format(arg))
ytURL = arg
elif opt in ("-o", "--odir"):
print("--odir={}".format(arg))
outdir = arg
elif opt in ("-m", "--maxframes"):
print("--maxframes={}".format(arg))
maxFrames = int(arg)
#
if ytURL is None:
print 'bad yt: shellScript.py -y <yturl> -o <odir> -m <maxframes>'
sys.exit()
#
if outdir is None:
print 'bad outdir: shellScript.py -y <yturl> -o <odir> -m <maxframes>'
sys.exit()
#
if False == isinstance(maxFrames, (int, long)):
print 'bad maxFrames: shellScript.py -y <yturl> -o <odir> -m <maxframes>'
sys.exit()
#
#
faceDet = cv2.CascadeClassifier(
"haarcascade/haarcascade_frontalface_default.xml")
faceDet2 = cv2.CascadeClassifier(
"haarcascade/haarcascade_frontalface_alt2.xml")
faceDet3 = cv2.CascadeClassifier(
"haarcascade/haarcascade_frontalface_alt.xml")
faceDet4 = cv2.CascadeClassifier(
"haarcascade/haarcascade_frontalface_alt_tree.xml")
#
pdata, pframes, pfacedims = getNewInstances(ytURL,
faceDet,
faceDet2,
faceDet3,
faceDet4,
maxCount=maxFrames)
#
headers = dict()
headers['Ocp-Apim-Subscription-Key'] = ms_key1
headers['Content-Type'] = 'application/octet-stream'
#
resultsDf = pd.DataFrame()
frameId = 0
for image in pframes:
print("posting frame %d of %d" % (frameId, len(pframes)))
resultMS = processRequest(image, headers)
#
if isinstance(resultMS, list):
for result in resultMS:
if isinstance(result, dict):
resFrameList = []
for res in result['scores'].items():
resFrameList.append(
(frameId, res[0], res[1],
result["faceRectangle"]['left'],
result["faceRectangle"]['top'],
result["faceRectangle"]['width'],
result["faceRectangle"]['height']))
appendDf = pd.DataFrame(resFrameList,
columns=[
"frameId", "emotionLabel",
"conf", "faceleft",
"facetop", "faceW", "faceH"
])
resultsDf = resultsDf.append(appendDf)
time.sleep(2)
frameId += 1
#
dfFaces = DplyFrame(resultsDf)
#
topFaces = (
dfFaces >> group_by(X.emotionLabel) >> sift(X.conf == X.conf.max()) >>
sift(X.frameId == X.frameId.min()) >> ungroup() >> group_by(
X.frameId) >> sift(X.conf == X.conf.max()) >> ungroup() >> arrange(
X.emotionLabel))
topFaces = topFaces.drop_duplicates()
#print(topFaces)
#
i = 0
for index, row in topFaces.iterrows():
print("saving emotion frame %d of %d" % (i, len(topFaces.index)))
#
emotion = row["emotionLabel"]
confid = int(row["conf"] * 100)
image = pframes[int(row["frameId"])]
faceL = row["faceleft"]
faceT = row["facetop"]
faceW = row["faceW"]
faceH = row["faceH"]
#
#save cropped face
imageW = image[faceT:faceT + faceH, faceL:faceL + faceW]
cv2.imwrite(
os.path.expanduser("%s/Cropped_%s.jpg" % (outdir, emotion)),
imageW)
#
cv2.rectangle(image, (faceL, faceT), (faceL + faceW, faceT + faceH),
color=(255, 0, 0),
thickness=5)
cv2.putText(image, emotion, (faceL, faceT - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
#
cv2.imwrite(os.path.expanduser("%s/box%s.jpg" % (outdir, emotion)),
image)
i += 1
from dplython import X, mutate, group_by, diamonds
diamonds = diamonds >> mutate(bin=X["Unnamed: 0"] % 5000)
gbinp = diamonds.groupby("bin")
gbind = diamonds >> group_by(X.bin)
# Test 1
gbinp["foo"] = gbinp.x.transform('mean')
gbind = gbind >> mutate(foo=X.x.mean())
print gbinp["foo"].equals(gbind["foo"])
# Test 2
gbinp["foo"] = gbinp.x.transform('mean') + gbinp.y.transform('mean')
gbind = gbind >> mutate(foo=X.x.mean() + X.y.mean())
print gbinp["foo"].equals(gbind["foo"])