I have got a pandas dataframe which looks like the following:
df.head()
categorized.Hashtags
0 icietmaintenant supyoga standuppaddleportugal ...
1 instapaysage bretagne labellebretagne bretagne...
2 bretagne lescrepescestlavie quimper bzh labret...
3 bretagne mer paysdiroise magnifique phare plou...
4 bateaux baiededouarnenez voiliers vieuxgreemen..
Now instead of using pandas get_dummmies() command I would like to use CountVectorizer to create the same output. Because get_dummies takes too much time.
df_x = df["categorized.Hashtags"]
vect = CountVectorizer(min_df=0.,max_df=1.0)
X = vect.fit_transform(df_x)
count_vect_df = pd.DataFrame(X.todense(), columns = vect.get_feature_names())
When I now output the respective data frame "count_vect_df" then the data frame contains a lot of columns which are empty/ contains only zero values. How can I avoid this?
Cheers,
Andi
From scikit-learn CountVectorizer docs:
Convert a collection of text documents to a matrix of token counts
This implementation produces a sparse representation of the counts
using scipy.sparse.csr_matrix.
The CountVectorizer returns a sparse-matrix, which contains most of zero values, where non-zero values represent the number of times that specific term has appeared in the particular document.
I am trying to do an exercise in pandas.
I have two dataframes. I need to compare few columns between both dataframes and change the value of one column in the first dataframe if the comparison is successful.
Dataframe 1:
Article Country Colour Buy
Pants Germany Red 0
Pull Poland Blue 0
Initially all my articles have the flag 'Buy' set to zero.
I have dataframe 2 that looks as:
Article Origin Colour
Pull Poland Blue
Dress Italy Red
I want to check if the article, country/origin and colour columns match (so check whether I can find the each article from dataframe 1 in dataframe two) and, if so, I want to put the flag 'Buy' to 1.
I trying to iterate through both dataframe with pyspark but pyspark daatframes are not iterable.
I thought about doing it in pandas but apaprently is a bad practise to change values during iteration.
Which code in pyspark or pandas would work to do what I need to do?
Thanks!
merge with an indicator then map the values. Make sure to drop_duplicates on the merge keys in the right frame so the merge result is always the same length as the original, and rename so we don't repeat the same information after the merge. No need to have a pre-defined column of 0s.
df1 = df1.drop(columns='Buy')
df1 = df1.merge(df2.drop_duplicates().rename(columns={'Origin': 'Country'}),
indicator='Buy', how='left')
df1['Buy'] = df1['Buy'].map({'left_only': 0, 'both': 1}).astype(int)
Article Country Colour Buy
0 Pants Germany Red 0
1 Pull Poland Blue 1
I have a TSV file that I loaded into a pandas dataframe to do some preprocessing and I want to find out which rows have a question in it, and output 1 or 0 in a new column. Since it is a TSV, this is how I'm loading it:
import pandas as pd
df = pd.read_csv('queries-10k-txt-backup', sep='\t')
Here's a sample of what it looks like:
QUERY FREQ
0 hindi movies for adults 595
1 are panda dogs real 383
2 asuedraw winning numbers 478
3 sentry replacement keys 608
4 rebuilding nicad battery packs 541
After dropping empty rows, duplicates, and the FREQ column(not needed for this), I wrote a simple function to check the QUERY column to see if it contains any words that make the string a question:
df_test = df.drop_duplicates()
df_test = df_test.dropna()
df_test = df_test.drop(['FREQ'], axis = 1)
def questions(row):
questions_list =
["what","when","where","which","who","whom","whose","why","why don't",
"how","how far","how long","how many","how much","how old","how come","?"]
if row['QUERY'] in questions_list:
return 1
else:
return 0
df_test['QUESTIONS'] = df_test.apply(questions, axis=1)
But once I check the new dataframe, even though it creates the new column, all the values are 0. I'm not sure if my logic is wrong in the function, I've used something similar with dataframe columns which just have one word and if it matches, it'll output a 1 or 0. However, that same logic doesn't seem to be working when the column contains a phrase/sentence like this use case. Any input is really appreciated!
If you wish to check exact matches of any substring from question_list and of a string from dataframe, you should use str.contains method:
questions_list = ["what","when","where","which","who","whom","whose","why",
"why don't", "how","how far","how long","how many",
"how much","how old","how come","?"]
pattern = "|".join(questions_list) # generate regex from your list
df_test['QUESTIONS'] = df_test['QUERY'].str.contains(pattern)
Simplified example:
df = pd.DataFrame({
'QUERY': ['how do you like it', 'what\'s going on?', 'quick brown fox'],
'ID': [0, 1, 2]})
Create a pattern:
pattern = '|'.join(['what', 'how'])
pattern
Out: 'what|how'
Use it:
df['QUERY'].str.contains(pattern)
Out[12]:
0 True
1 True
2 False
Name: QUERY, dtype: bool
If you're not familiar with regexes, there's a quick python re reference. Fot symbol '|', explanation is
A|B, where A and B can be arbitrary REs, creates a regular expression that will match either A or B. An arbitrary number of REs can be separated by the '|' in this way
IIUC, you need to find if the first word in the string in the question list, if yes return 1, else 0. In your function, rather than checking if the entire string is in question list, split the string and check if the first element is in question list.
def questions(row):
questions_list = ["are","what","when","where","which","who","whom","whose","why","why don't","how","how far","how long","how many","how much","how old","how come","?"]
if row['QUERY'].split()[0] in questions_list:
return 1
else:
return 0
df['QUESTIONS'] = df.apply(questions, axis=1)
You get
QUERY FREQ QUESTIONS
0 hindi movies for adults 595 0
1 are panda dogs real 383 1
2 asuedraw winning numbers 478 0
3 sentry replacement keys 608 0
4 rebuilding nicad battery packs 541 0
I have a dataframe:
a b c
0 1 2 3
1 1 1 1
2 3 7 NaN
3 2 3 5
...
I want to fill column "three" inplace (update the values) where the values are NaN using a machine learning algorithm.
I don't know how to do it inplace. Sample code:
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
df=pd.DataFrame([range(3), [1, 5, np.NaN], [2, 2, np.NaN], [4,5,9], [2,5,7]],columns=['a','b','c'])
x=[]
y=[]
for row in df.iterrows():
index,data = row
if(not pd.isnull(data['c'])):
x.append(data[['a','b']].tolist())
y.append(data['c'])
model = LinearRegression()
model.fit(x,y)
#this line does not do it in place.
df[~df.c.notnull()].assign(c = lambda x:model.predict(x[['a','b']]))
But this gives me a copy of the dataframe. Only option I have left is using a for loop however, I don't want to do that. I think there should be more pythonic way of doing it using pandas. Can someone please help? Or is there any other way of doing this?
You'll have to do something like :
df.loc[pd.isnull(df['three']), 'three'] = _result of model_
This modifies directly dataframe df
This way you first filter the dataframe to keep the slice you want to modify (pd.isnull(df['three'])), then from that slice you select the column you want to modify (three).
On the right hand side of the equal, it expects to get an array / list / series with the same number of lines than the filtered dataframe ( in your example, one line)
You may have to adjust depending on what your model returns exactly
EDIT
You probably need to do stg like this
pred = model.predict(df[['a', 'b']])
df['pred'] = model.predict(df[['a', 'b']])
df.loc[pd.isnull(df['c']), 'c'] = df.loc[pd.isnull(df['c']), 'pred']
Note that a significant part of the issue comes from the way you are using scikit learn in your example. You need to pass the whole dataset to the model when you predict.
The simplest way is yo transpose first, then forward fill/backward fill at your convenience.
df.T.ffill().bfill().T
Below is the input pandas dataframe I have.
I want to find the frequency of unigrams & bigrams. A sample of what I am expecting is shown below
How to do this using nltk or scikit learn?
I wrote the below code which takes a string as input. How to extend it to series/dataframe?
from nltk.collocations import *
desc='john is a guy person you him guy person you him'
tokens = nltk.word_tokenize(desc)
bigram_measures = nltk.collocations.BigramAssocMeasures()
finder = BigramCollocationFinder.from_words(tokens)
finder.ngram_fd.viewitems()
If your data is like
import pandas as pd
df = pd.DataFrame([
'must watch. Good acting',
'average movie. Bad acting',
'good movie. Good acting',
'pathetic. Avoid',
'avoid'], columns=['description'])
You could use the CountVectorizer of the package sklearn:
from sklearn.feature_extraction.text import CountVectorizer
word_vectorizer = CountVectorizer(ngram_range=(1,2), analyzer='word')
sparse_matrix = word_vectorizer.fit_transform(df['description'])
frequencies = sum(sparse_matrix).toarray()[0]
pd.DataFrame(frequencies, index=word_vectorizer.get_feature_names(), columns=['frequency'])
Which gives you :
frequency
good 3
pathetic 1
average movie 1
movie bad 2
watch 1
good movie 1
watch good 3
good acting 2
must 1
movie good 2
pathetic avoid 1
bad acting 1
average 1
must watch 1
acting 1
bad 1
movie 1
avoid 1
EDIT
fit will just "train" your vectorizer : it will split the words of your corpus and create a vocabulary with it. Then transform can take a new document and create vector of frequency based on the vectorizer vocabulary.
Here your training set is your output set, so you can do both at the same time (fit_transform). Because you have 5 documents, it will create 5 vectors as a matrix. You want a global vector, so you have to make a sum.
EDIT 2
For big dataframes, you can speed up the frequencies computation by using:
frequencies = sum(sparse_matrix).data
or
frequencies = sparse_matrix.sum(axis=0).T