I have a data file containing different foetal ultrasound measurements. The measurements are collected at different points during pregnancy, like so:
PregnancyID MotherID gestationalAgeInWeeks abdomCirc
0 0 14 150
0 0 21 200
1 1 20 294
1 1 25 315
1 1 30 350
2 2 8 170
2 2 9 180
2 2 18 NaN
Following this answer to a previous questions I had asked, I used this code to summarise the ultrasound measurements using the maximum measurement recorded in a single trimester (13 weeks):
(df.assign(tm = (df['gestationalAgeInWeeks']+ 13 - 1 )// 13))
.drop(columns = 'gestationalAgeInWeeks')
.groupby(['MotherID', 'PregnancyID','tm'])
.agg('max')
.unstack()
)
This results in the following output:
tm 1 2 3
MotherID PregnancyID
0 0 NaN 200.0 NaN
1 1 NaN 294.0 350.0
2 2 180.0 NaN NaN
However, MotherID and PregnancyID no longer appear as columns in the output of df.info(). Similarly, when I output the dataframe to a csv file, I only get columns 1,2 and 3. The id columns only appear when running df.head() as can be seen in the dataframe above.
I need to preserve the id columns as I want to use them to merge this dataframe with another one using the ids. Therefore, my question is, how do I preserve these id columns as part of my dataframe after running the code above?
Chain that with reset_index:
(df.assign(tm = (df['gestationalAgeInWeeks']+ 13 - 1 )// 13)
# .drop(columns = 'gestationalAgeInWeeks') # don't need this
.groupby(['MotherID', 'PregnancyID','tm'])['abdomCirc'] # change here
.max().add_prefix('abdomCirc_') # here
.unstack()
.reset_index() # and here
)
Or a more friendly version with pivot_table:
(df.assign(tm = (df['gestationalAgeInWeeks']+ 13 - 1 )// 13)
.pivot_table(index= ['MotherID', 'PregnancyID'], columns='tm',
values= 'abdomCirc', aggfunc='max')
.add_prefix('abdomCirc_') # remove this if you don't want the prefix
.reset_index()
)
Output:
tm MotherID PregnancyID abdomCirc_1 abdomCirc_2 abdomCirc_3
0 abdomCirc_0 abdomCirc_0 NaN 200.0 NaN
1 abdomCirc_1 abdomCirc_1 NaN 315.0 350.0
2 abdomCirc_2 abdomCirc_2 180.0 NaN NaN
I have a dataframe called 'Adj_Close' which looks like this:
AAPL TSLA GOOG
0 3.478462 NaN NaN
1 3.185191 NaN NaN
2 3.231803 NaN NaN
3 2.952128 NaN NaN
4 3.091966 NaN NaN
... ... ... ...
5005 261.779999 333.040009 1295.339966
5006 266.369995 336.339996 1306.689941
5007 264.290009 328.920013 1313.550049
5008 267.839996 331.290009 1312.989990
5009 267.250000 329.940002 1304.959961
I want to save each column ('AAPL', 'TSLA' & 'GOOG') in a new dataframe.
The code should look like this:
i = 0
n = 3
while i < n:
df_{i} = Adj_Close.iloc[:,i]
i += 1
Unfortunately it is the wrong syntax. I hope someone can help me...
The natural way to do that in python would be to create an array of dataframes, as in:
dataframes = []
for col in df.columns:
new_df = pd.DataFrame(df[col])
dataframes.append(new_df)
The result is an array (dataframes) that contains three separate data frames - one for Google, one for Tesla, and one for Apple.
[ One can also define new variables using
globals()[my_var_name] = <some_value>
But I don't believe that's what you're looking for.
]
Currently i'm working on a Livetiming-Software for a motorsport-application. Therefore i have to crawl a Livetiming-Webpage and copy the Data to a big Dataframe. This Dataframe is the source of several diagramms i want to make. To keep my Dataframe up to date, i have to crawl the webpage very often.
I can download the Data and save them as a Panda.Dataframe. But my Problem is step from the downloaded DataFrame to the Big Dataframe, that includes all the Data.
import pandas as pd
import numpy as np
df1= pd.DataFrame({'Pos':[1,2,3,4,5,6],'CLS':['V5','V5','V5','V4','V4','V4'],
'Nr.':['13','700','30','55','24','985'],
'Zeit':['1:30,000','1:45,000','1:50,000','1:25,333','1:13,366','1:17,000'],
'Laps':['1','1','1','1','1','1']})
df2= pd.DataFrame({'Pos':[1,2,3,4,5,6],'CLS':['V5','V5','V5','V4','V4','V4'],
'Nr.':['13','700','30','55','24','985'],
'Zeit':[np.nan,np.nan,np.nan,np.nan,np.nan,np.nan,],
'Laps':['2','2','2','2','2','2']})
df3= pd.DataFrame({'Pos':[1,2,3,4,5,6],'CLS':['V5','V5','V5','V4','V4','V4'],
'Nr.':['13','700','30','55','24','985'],
'Zeit':['1:31,000','1:41,000','1:51,000','1:21,333','1:11,366','1:11,000'],
'Laps':['2','2','2','2','2','2']})
df1.set_index(['CLS','Nr.','Laps'],inplace=True)
df2.set_index(['CLS','Nr.','Laps'],inplace=True)
df3.set_index(['CLS','Nr.','Laps'],inplace=True)
df1 shows a Dataframe from previous laps.
df2 shows a Dataframe in the second lap. The Lap is not completed, so i have a nan.
df3 shows a Dataframe after the second lap is completed.
My target is to have just one row for each Lap per Car per Class.
Either i have the problem, that i have duplicates with incomplete Laps or all date get overwritten.
I hope that someone can help me with this problem.
Thank you so far.
MrCrunsh
If I understand your problem correctly, your issue is that you have overlapping data for the second lap: information while the lap is still in progress and information after it's over. If you want to put all the information for a given lap in one row, I'd suggest use multi-index columns or changing the column names to reflect the difference between measurements during and after laps.
df = pd.concat([df1, df3])
df = pd.concat([df, df2], axis=1, keys=['after', 'during'])
The result will look like this:
after during
Pos Zeit Pos Zeit
CLS Nr. Laps
V4 24 1 5 1:13,366 NaN NaN
2 5 1:11,366 5.0 NaN
55 1 4 1:25,333 NaN NaN
2 4 1:21,333 4.0 NaN
985 1 6 1:17,000 NaN NaN
2 6 1:11,000 6.0 NaN
V5 13 1 1 1:30,000 NaN NaN
2 1 1:31,000 1.0 NaN
30 1 3 1:50,000 NaN NaN
2 3 1:51,000 3.0 NaN
700 1 2 1:45,000 NaN NaN
2 2 1:41,000 2.0 NaN
I want to extract dataframe from HTML using URL.
The page contains 59 table/dataframe.
I want to extract 1 particular table which can be identified by its ID "ctl00_Menu1"
Following is my trail which is giving error.
import pandas as pd
df = pd.read_html("http://eciresults.nic.in/statewiseS12.htm?st=S12",attrs = {'id': 'ctl00_Menu1'})
As this is my very early stage in python so can be simple solution but I am unable to find. appreciate help.
I would look at how the URL passes params and probably try to read a dataframe directly from it. I'm unsure if you are trying to develop a function or a script or just exercising.
If you do (notice the 58 at the end of the url)
df = pd.read_html("http://eciresults.nic.in/statewiseS12.htm?st=S1258",attrs = {'id':
'ctl00_Menu1'})
It works and gives you table 59.
[ 0 1 2 \
0 Partywise Partywise NaN
1 Partywise NaN NaN
2 Constituencywise-All Candidates NaN NaN
3 Constituencywise Trends NaN NaN
3 4 5 \
0 Constituencywise-All Candidates Constituencywise-All Candidates NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
6 7
0 Constituencywise Trends Constituencywise Trends
1 NaN NaN
2 NaN NaN
3 NaN NaN ]
Unsure if that's the table you want to extract, but most of the time it's easier to pass it as a url parameter. If you try it without the 58 it works too, I believe the 'ElectionResult' argument might not be a table classifier hence why you can't find any tables with that name.
I have a pandas Dataframe containing EOD financial data (OHLC) for analysis.
I'm using https://github.com/cirla/tulipy library to generate technical indicator values, that have a certain timeperiod as option. For Example. ADX with timeperiod=5 shows ADX for last 5 days.
Because of this timeperiod, the generated array with indicator values is always shorter in length than the Dataframe. Because the prices of first 5 days are used to generate ADX for day 6..
pdi14, mdi14 = ti.di(
high=highData, low=lowData, close=closeData, period=14)
df['mdi_14'] = mdi14
df['pdi_14'] = pdi14
>> ValueError: Length of values does not match length of index
Unfortunately, unlike TA-LIB for example, this tulip library does not provide NaN-values for these first couple of empty days...
Is there an easy way to prepend these NaN to the ndarray?
Or insert into df at a certain index & have it create NaN for the rows before it automatically?
Thanks in advance, I've been researching for days!
Maybe make the shift yourself in the code ?
period = 14
pdi14, mdi14 = ti.di(
high=highData, low=lowData, close=closeData, period=period
)
df['mdi_14'] = np.NAN
df['mdi_14'][period - 1:] = mdi14
I hope they will fill the first values with NAN in the lib in the future. It's dangerous to leave time series data like this without any label.
Full MCVE
df = pd.DataFrame(1, range(10), list('ABC'))
a = np.full((len(df) - 6, df.shape[1]), 2)
b = np.full((6, df.shape[1]), np.nan)
c = np.row_stack([b, a])
d = pd.DataFrame(c, df.index, df.columns)
d
A B C
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
5 NaN NaN NaN
6 2.0 2.0 2.0
7 2.0 2.0 2.0
8 2.0 2.0 2.0
9 2.0 2.0 2.0
The C version of the tulip library includes a start function for each indicator (reference: https://tulipindicators.org/usage) that can be used to determine the output length of an indicator given a set of input options. Unfortunately, it does not appear that the python bindings library, tulipy, includes this functionality. Instead you have to resort to dynamically reassigning your index values to align the output with the original DataFrame.
Here is an example that uses the price series from the tulipy docs:
#Create the dataframe with close prices
prices = pd.DataFrame(data={81.59, 81.06, 82.87, 83, 83.61, 83.15, 82.84, 83.99, 84.55,
84.36, 85.53, 86.54, 86.89, 87.77, 87.29}, columns=['close'])
#Compute the technical indicator using tulipy and save the result in a DataFrame
bbands = pd.DataFrame(data=np.transpose(ti.bbands(real = prices['close'].to_numpy(), period = 5, stddev = 2)))
#Dynamically realign the index; note from the tulip library documentation that the price/volume data is expected be ordered "oldest to newest (index 0 is oldest)"
bbands.index += prices.index.max() - bbands.index.max()
#Put the indicator values with the original DataFrame
prices[['BBANDS_5_2_low', 'BBANDS_5_2_mid', 'BBANDS_5_2_up']] = bbands
prices.head(15)
close BBANDS_5_2_low BBANDS_5_2_mid BBANDS_5_2_up
0 81.06 NaN NaN NaN
1 81.59 NaN NaN NaN
2 82.87 NaN NaN NaN
3 83.00 NaN NaN NaN
4 83.61 80.530042 82.426 84.321958
5 83.15 81.494061 82.844 84.193939
6 82.84 82.533343 83.094 83.654657
7 83.99 82.471983 83.318 84.164017
8 84.55 82.417750 83.628 84.838250
9 84.36 82.435203 83.778 85.120797
10 85.53 82.511331 84.254 85.996669
11 86.54 83.142618 84.994 86.845382
12 86.89 83.536488 85.574 87.611512
13 87.77 83.870324 86.218 88.565676
14 87.29 85.288871 86.804 88.319129