I am trying to make a dataframe with Historical data of daily No. of stock Advancing and declining with their respective volumes of Nifty 50 index.
Being new to python I am having trouble handling pandas dataframe and conditions.
Below is the code that I wrote, but the output's columns are wrong:
import datetime
from datetime import date, timedelta
import nsepy as ns
from nsepy.derivatives import get_expiry_date
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
#setting default dates
end_date = date.today()
start_date = end_date - timedelta(365)
#Deriving the names of 50 stocks in Nifty 50 Index
nifty_50 = pd.read_html('https://en.wikipedia.org/wiki/NIFTY_50')
nifty50_symbols = nifty_50[1][1]
results = []
for x in nifty50_symbols:
data = ns.get_history(symbol = x, start=start_date, end=end_date)
results.append(data)
df = pd.concat(results)
output = []
for x in df.index:
Dates = df[df.index == x]
adv = 0
dec = 0
net = 0
advol = 0
devol = 0
netvol = 0
for s in Dates['Symbol']:
y = Dates[Dates['Symbol'] == s]
#print(y.loc[x,'Close'])
cclose = y.loc[x,'Close']
#print(cclose)
copen = y.loc[x,'Open']
#print(copen)
cvol = y.loc[x,'Volume']
if cclose > copen:
adv = adv + 1
advol = advol + cvol
elif copen > cclose:
dec = dec + 1
devol = devol + cvol
else:
net = net + 1
netvol = netvol + cvol
data = [x,adv,dec,advol,devol]
output.append(data)
final = pd.DataFrame(output, columns = {'Date','Advance','Decline','Adv_Volume','Dec_Volume'})
print(final)
Output:
Dec_Volume Adv_Volume Date Decline Advance
0 2017-02-06 27 23 88546029 70663663
1 2017-02-07 15 35 53775268 127004815
2 2017-02-08 27 23 76150502 96895043
3 2017-02-09 20 30 48815099 121956144
4 2017-02-10 19 31 47713187 156262469
5 2017-02-13 23 27 78460358 86575050
6 2017-02-14 15 35 65543372 100474945
7 2017-02-15 13 37 35055563 160091302
8 2017-02-16 35 15 114283658 73082870
9 2017-02-17 22 28 91383781 193246678
10 2017-02-20 34 16 100148171 54036281
11 2017-02-21 29 21 87434834 75182662
12 2017-02-22 13 37 77086733 148499613
13 2017-02-23 20 29 104469151 192787014
14 2017-02-27 13 37 41823692 140518994
15 2017-02-28 21 29 76949655 142799485
As you can see from output that the column names do not match with that data under them. Why is this happening and how do I fix it?
If I print the value of Output list after the series of loops are over then the data looks exactly the way I want it to be(as far as a novice like me can see). The problem is happening when I am converting the Output list into a DataFrame.
I think the solution is simply to pass your column names as a Python list (using []), which has a well-defined element order, rather than as a set ({}) of unordered elements:
final = pd.DataFrame(output, columns = ['Date','Advance','Decline','Adv_Volume','Dec_Volume'])
Related
Good morning all!
I have a Pandas df and Im trying to create a monthly box and whisker of 30 years ofdata.
DataFrame
datetime year month day hour lon lat
0 3/18/1986 10:17 1986 3 18 10 -124.835 46.540
1 6/7/1986 13:38 1986 6 7 13 -121.669 46.376
2 7/17/1986 20:56 1986 7 17 20 -122.436 48.044
3 7/26/1986 2:46 1986 7 26 2 -123.071 48.731
4 8/2/1986 19:54 1986 8 2 19 -123.654 48.480
Trying to see the mean amount of occurrences in X month, the median, and the max/min occurrence ( and date of max and min)..
Ive been playing around with pandas.DataFrame.groupby() but dont fully understand it.
I have grouped the date by month and day occurrences. I like this format:
Code:
df = pd.read_csv(masterCSVPath)
months = df['month']
test = df.groupby(['month','day'])['day'].count()
output: ---->
month day
1 1 50
2 103
3 97
4 29
5 60
...
12 27 24
28 7
29 17
30 18
31 9
So how can i turn that df above into a box/whisker plot?
The x-axis i want to be months..
y axis == occurrences
Try this (without doing groupby):
import matplotlib.pyplot as plt
import seaborn as sns
sns.boxplot(x = 'month', y = 'day', data = df)
In case you want the months to be in Jan, Feb format then try this:
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
df['month_new'] = df['datetime'].dt.strftime('%b')
sns.boxplot(x = 'month_new', y = 'day', data = df)
I'm pretty new in python / pandas, so its probably pretty simple question...but I can't handle it:
I have two dataframe loaded from Oracle SQL. One with 300 rows / 2 column and second with one row/one column. I would like to add column from second dataset to the first for each row as new column. But I can only get it for the first row and the others are NaN.
`import cx_Oracle
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.externals import joblib
dsn_tns = cx_Oracle.makedsn('127.0.1.1', '1521', 'orcl')
conn = cx_Oracle.connect(user='MyName', password='MyPass', dsn=dsn_tns)
d_score = pd.read_sql_query(
'''
SELECT
ID
,RESULT
,RATIO_A
,RATIO_B
from ORCL_DATA
''', conn) #return 380 rows
d_score['ID'] = d_score['ID'].astype(int)
d_score['RESULT'] = d_score['RESULT'].astype(int)
d_score['RATIO_A'] = d_score['RATIO_A'].astype(float)
d_score['RATIO_B'] = d_score['RATIO_B'].astype(float)
d_score_features = d_score.iloc [:,2:4]
#d_train_target = d_score.iloc[:,1:2] #target is RESULT
DM_train = xgb.DMatrix(data= d_score_features)
loaded_model = joblib.load("bst.dat")
pred = loaded_model.predict(DM_train)
i = pd.DataFrame({'ID':d_score['ID'],'Probability':pred})
print(i)
s = pd.read_sql_query('''select max(id_process) as MAX_ID_PROCESS from PROCESS''',conn) #return only 1 row
m =pd.DataFrame(data=s, dtype=np.int64,columns = ['MAX_ID_PROCESS'] )
print(m)
i['new'] = m ##Trying to add MAX_ID_PROCESS to all rows
print(i)
i =
ID Probability
0 20101 0.663083
1 20105 0.486774
2 20106 0.441300
3 20278 0.703176
4 20221 0.539185
....
379 20480 0.671976
m =
MAX_ID_PROCESS
0 274
i =
ID_MATCH Probability new
0 20101 0.663083 274.0
1 20105 0.486774 NaN
2 20106 0.441300 NaN
3 20278 0.703176 NaN
4 20221 0.539185 NaN
I need value 'new' for all rows...
Since your second dataframe is only having one value, you can assign it like this:
df1['new'] = df2.MAX_ID_PROCESS[0]
# Or using .loc
df1['new'] = df2.MAX_ID_PROCESS.loc[0]
In your case, it should be:
i['new'] = m.MAX_ID_PROCESS[0]
You should now see:
ID Probability new
0 20101 0.663083 274.0
1 20105 0.486774 274.0
2 20106 0.441300 274.0
3 20278 0.703176 274.0
4 20221 0.539185 274.0
As we know that we can append one column of dataframe1 to dataframe2 as new column using the code: dataframe2["new_column_name"] = dataframe1["column_to_copy"].
We can extend this approach to solve your problem.
import pandas as pd
import numpy as np
df1 = pd.DataFrame()
df1["ColA"] = [1, 12, 32, 24,12]
df1["ColB"] = [23, 11, 6, 45,25]
df1["ColC"] = [10, 25, 3, 23,15]
print(df1)
Output:
ColA ColB ColC
0 1 23 10
1 12 11 25
2 32 6 3
3 24 45 23
4 12 25 15
Now we create a new dataframe and add a row to it.
df3 = pd.DataFrame()
df3["ColTest"] = [1]
Now we store the value of the first row of the second dataframe as we want to add it to all the rows in dataframe1 as a new column:
val = df3.iloc[0]
print(val)
Output:
ColTest 1
Name: 0, dtype: int64
Now, we will store this value for as many rows as we have in dataframe1.
rows = len(df1)
for row in range(rows):
df3.loc[row]=val
print(df3)
Output:
ColTest
0 1
1 1
2 1
3 1
4 1
Now we will append this column to the first dataframe and solve your problem.
df["ColTest"] = df3["ColTest"]
print(df)
Output:
ColA ColB ColC ColTest
0 1 23 10 1
1 12 11 25 1
2 32 6 3 1
3 24 45 23 1
4 12 25 15 1
14 [2018-03-14, 2018-03-13, 2017-03-06, 2017-02-13]
15 [2017-07-26, 2017-06-09, 2017-02-24]
16 [2018-09-06, 2018-07-06, 2018-07-04, 2017-10-20]
17 [2018-10-03, 2018-09-13, 2018-09-12, 2018-08-3]
18 [2017-02-08]
this is my data, every ID has it's own dates that range between 2017-02-05 and 2018-06-30. I need to split dates into 5 time ranges of 4 months each, so that for the first 4 months every ID should have dates only in that time range (from 2017-02-05 to 2017-06-05), like this
14 [2017-03-06, 2017-02-13]
15 [2017-02-24]
16 [null] # or delete empty rows, it doesn't matter
17 [null]
18 [2017-02-08]
then for 2017-06-05 to 2017-10-05 and so on for every 4 month ranges. Also I can't use nested for loops because the data is too big. This is what I tried so far
months_4 = individual_dates.copy()
for _ in months_4['Date']:
_ = np.where(pd.to_datetime(_) <= pd.to_datetime('2017-9-02'), _, np.datetime64('NaT'))
and
months_8 = individual_dates.copy()
range_8 = pd.date_range(start='2017-9-02', end='2017-11-02')
for _ in months_8['Date']:
_ = _[np.isin(_, range_8)]
achieved absolutely no result, data stays the same no matter what
update: I did what you said
individual_dates['Date'] = individual_dates['Date'].str.strip('[]').str.split(', ')
df = pd.DataFrame({
'Date' : list(chain.from_iterable(individual_dates['Date'].tolist())),
'ID' : individual_dates['ClientId'].repeat(individual_dates['Date'].str.len())
})
df
and here is the result
Date ID
0 '2018-06-30T00:00:00.000000000' '2018-06-29T00... 14
1 '2017-03-28T00:00:00.000000000' '2017-03-27T00... 15
2 '2018-03-14T00:00:00.000000000' '2018-03-13T00... 16
3 '2017-12-14T00:00:00.000000000' '2017-03-28T00... 17
4 '2017-05-30T00:00:00.000000000' '2017-05-22T00... 18
5 '2017-03-28T00:00:00.000000000' '2017-03-27T00... 19
6 '2017-03-27T00:00:00.000000000' '2017-03-26T00... 20
7 '2017-12-15T00:00:00.000000000' '2017-11-20T00... 21
8 '2017-07-05T00:00:00.000000000' '2017-07-04T00... 22
9 '2017-12-12T00:00:00.000000000' '2017-04-06T00... 23
10 '2017-05-21T00:00:00.000000000' '2017-05-07T00... 24
For better performance I suggest convert list to column - flatten it and then filtering by isin with boolean indexing:
from itertools import chain
df = pd.DataFrame({
'Date' : list(chain.from_iterable(individual_dates['Date'].tolist())),
'ID' : individual_dates['ID'].repeat(individual_dates['Date'].str.len())
})
range_8 = pd.date_range(start='2017-02-05', end='2017-06-05')
df['Date'] = pd.to_datetime(df['Date'])
df = df[df['Date'].isin(range_8)]
print (df)
Date ID
0 2017-03-06 14
0 2017-02-13 14
1 2017-02-24 15
4 2017-02-08 18
I'm trying to scrape an html table using beautiful soup and import it into pandas -- http://www.baseball-reference.com/teams/NYM/2017.shtml -- the "Team Batting" table.
Finding the table is no problem:
table = soup.find('div', attrs={'class': 'overthrow table_container'})
table_body = table.find('tbody')
Finding the rows of data isn't a problem either:
for i in table.findAll('tr')[2]: #increase to 3 to get next row in table...
print(i.get_text())
And I can even find the header names:
table_head = table.find('thead')
for i in table_head.findAll('th'):
print(i.get_text())
Now I'm having trouble putting everything together into a data frame. Here's what I have so far:
header = []
for th in table_head.findAll('th'):
key = th.get_text()
header.append(key)
row= []
for tr in table.findAll('tr')[2]:
value = tr.get_text()
row.append(value)
od = OrderedDict(zip(head, row))
df = pd.DataFrame(d1, index=[0])
This only works for one row at a time. My question is how can I do this for every row in the table at the same time?
I have tested that the below will work for your purposes. Basically you need to create a list, loop over the players, use that list to populate a DataFrame. It is advisable to not create the DataFrame row by row as that will probably be significantly slower.
import collections as co
import pandas as pd
from bs4 import BeautifulSoup
with open('team_batting.html','r') as fin:
soup = BeautifulSoup(fin.read(),'lxml')
table = soup.find('div', attrs={'class': 'overthrow table_container'})
table_body = table.find('tbody')
table_head = table.find('thead')
header = []
for th in table_head.findAll('th'):
key = th.get_text()
header.append(key)
# loop over table to find number of rows with '' in first column
endrows = 0
for tr in table.findAll('tr'):
if tr.findAll('th')[0].get_text() in (''):
endrows += 1
rows = len(table.findAll('tr'))
rows -= endrows + 1 # there is a pernicious final row that begins with 'Rk'
list_of_dicts = []
for row in range(rows):
the_row = []
try:
table_row = table.findAll('tr')[row]
for tr in table_row:
value = tr.get_text()
the_row.append(value)
od = co.OrderedDict(zip(header,the_row))
list_of_dicts.append(od)
except AttributeError:
continue
df = pd.DataFrame(list_of_dicts)
This solution uses only pandas, but it cheats a little by knowing in advance that the team batting table is the tenth table. With that knowledge, the following uses pandas's read_html function and grabbing the tenth DataFrame from the list of returned DataFrame objects. The remaining after that is just some data cleaning:
import pandas as pd
url = 'http://www.baseball-reference.com/teams/NYM/2017.shtml'
# Take 10th dataframe
team_batting = pd.read_html(url)[9]
# Take columns whose names don't contain "Unnamed"
team_batting.drop([x for x in team_batting.columns if 'Unnamed' in x], axis=1, inplace=True)
# Remove the rows that are just a copy of the headers/columns
team_batting = team_batting.ix[team_batting.apply(lambda x: x != team_batting.columns,axis=1).all(axis=1),:]
# Take out the Totals rows
team_batting = team_batting.ix[~team_batting.Rk.isnull(),:]
# Get a glimpse of the data
print(team_batting.head(5))
# Rk Pos Name Age G PA AB R H 2B ... OBP SLG OPS OPS+ TB GDP HBP SH SF IBB
# 0 1 C Travis d'Arnaud 28 12 42 37 6 10 2 ... .357 .541 .898 144 20 1 1 0 0 1
# 1 2 1B Lucas Duda* 31 13 50 42 4 10 2 ... .360 .571 .931 153 24 1 0 0 0 2
# 2 3 2B Neil Walker# 31 14 62 54 5 12 3 ... .306 .278 .584 64 15 2 0 0 1 0
# 3 4 SS Asdrubal Cabrera# 31 15 67 63 10 17 2 ... .313 .397 .710 96 25 0 0 0 0 0
# 4 5 3B Jose Reyes# 34 15 59 53 3 5 2 ... .186 .132 .319 -9 7 0 0 0 0 0
I hope this helps.
I am looking to append several columns to a dataframe.
Let's say I start with this:
import pandas as pd
dfX = pd.DataFrame({'A': [1,2,3,4],'B': [5,6,7,8],'C': [9,10,11,12]})
dfY = pd.DataFrame({'D': [13,14,15,16],'E': [17,18,19,20],'F': [21,22,23,24]})
I am able to append the dfY columns to dfX by defining the new columns in list form:
dfX[[3,4]] = dfY.iloc[:,1:3].copy()
...but I would rather do so this way:
dfX.iloc[:,3:4] = dfY.iloc[:,1:3].copy()
The former works! The latter executes, returns no errors, but does not alter dfX.
Are you looking for
dfX = pd.concat([dfX, dfY], axis = 1)
It returns
A B C D E F
0 1 5 9 13 17 21
1 2 6 10 14 18 22
2 3 7 11 15 19 23
3 4 8 12 16 20 24
And you can append several dataframes in this like pd.concat([dfX, dfY, dfZ], axis = 1)
If you need to append say only column D and E from dfY to dfX, go for
pd.concat([dfX, dfY[['D', 'E']]], axis = 1)