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)
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);
# 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())
# bind_rows(other, join='outer', ignore_index=False)
# pandas.concat([df, other], join=join, ignore_index=ignore_index, axis=0)
a >> bind_rows(b, join='inner')
a >> bind_rows(b, join='outer')
# bind_cols() - joining DataFrames "horizontally"
# bind_cols(other, join='outer', ignore_index=False)
# pandas.concat([df, other], join=join, ignore_index=ignore_index, axis=1)
a >> bind_cols(b)
# Summarization
# summarize(**kwargs) takes an arbitrary number of keyword arguments that will
# return new columns labeled with the keys that are summary functions of columns
# in the original DataFrame.
diamonds >> summarize(price_mean=X.price.mean(), price_std=X.price.std())
diamonds >> group_by('cut') >> summarize(price_mean=X.price.mean(), price_std=X.price.std())
# summarize_each(function_list, *columns) is a more general summarization function.
# It takes a list of summary functions to apply as its first argument and then a
# list of columns to apply the summary functions to. Columns can be specified with
# either symbolic, string label, or integer position like in the selection functions
# for convenience.
diamonds >> summarize_each([np.mean, np.var], X.price, 'depth')
diamonds >> group_by(X.cut) >> summarize_each([np.mean, np.var], X.price, 4)
# Summary functions
# mean(series)
diamonds >> groupby(X.cut) >> summarize(price_mean=mean(X.price))
# first(series, order_by=None)
diamonds >> groupby(X.cut) >> summarize(price_first=first(X.price))
"""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)
plt.show()
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
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