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Kantatu Coleccao Completa
List of Indian authors
A
Asokavalli Vaiyapuri
Venkataraman Achuthan
Kunjunni Krishnamurthy
Neelakantan Venkataraman
Kannan Ramakrishnan
Ponnazhagi Ramakrishnan
Gangadharan Pillai
Nalini Bhushan
Dattatreya Hosabale
B
Dharamvir Barot
Arjun Mathur
C
Dhashiraj Chote
Chetan Bhagat
Tripti Gokhale
D
Dinanath Desai
E
Abhishek Agarwal
Sanjay Rawal
Vikram Chandra
Arup Maharana
Rohit Bhargava
Annie Dillard
F
Gautam Ghosh
H
Hetal Vijan
Himalayaneni
Hiren Bhakta Pritam
J
Jyotirmaya Chandra
K
Kanchan Chaudhary
Kamala Chandra Das
Sally Thomas
L
Laxman Reddy
Laxmi Narayan Tripathi
M
Neehar Mushtaq
P
Piyush Chawla
Ashwin Sanghi
R
Ranjini Srikanth
Reena Pawar
S
Sagarika Ghosh
Shashi Deshpande
Basant Narayan Majumdar
Sunita Deshpande
T
Trijita Singh
Trupti Bhattacharya
V
Vishwanath Tulshyan
W
Aruna Ray
Ram Sharan
Vir Sanghvi
Z
Zoya Akhtar
References
Notes
Bibliography
Indian- Literature
Authors
Category:Lists of Indian writersVars
Child Views
Sty https://4c70.com/wp-content/uploads/2022/06/chercypr.pdf
[mp3+g]: Karaoke Kantatu – Colecção Completa
Another copy complete for free!. More versions to come! .
Translated to English by Google.
A:
I converted it to 7 files and it worked!
Q:
scipy.stats.multinom() returns error: array cannot be converted to float
I have a shape (7480, 47) data frame with observations over multiple trials.
I am using the multinomial distribution function to calculate the probability that the observations were drawn from a specific model.
This was my code:
# Importing my data as a data frame:
data_all = pd.read_csv(r’C:\Users\Garness\Desktop\Song_Collection_Changes.csv’, header=None)
# Selecting non-missing data
data_all = data_all[(data_all.Band == “Rock”) & (data_all.Model == “Tiger”) & (data_all.Genre == “Alternative”)]
# Removing rows with observations of 100%
data_all.dropna()
# Calculating the number of observations per genre in the dataset
data_all_grp = data_all.groupby(‘Genre’)
data_all_grp.count()
# Calculating the number of observations per model in the dataset
data_all_grp = data_all_grp.groupby(‘Model’)
data_all_grp.count()
# Creating a count data frame that will be used for the multinom() function
data_all_grp = data_all_grp.to_frame(index=False)
data_all_grp.to_csv(“C:\Users\Garness\Desktop\Song_Collection_Changes_counts.csv”, sep=’ ‘, encoding=’utf-8’)
counts = data_all_grp.to_frame().fillna(0)
counts
# Counting the number of observations for each Genre and Model
data_all_grp[‘counts’].fillna(0, inplace=True)
data_all_grp[‘counts’]
model_
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