My desired output is the following:
count tally
1 2 //
2 3 ///
3 5 /////
4 3 ///
5 2 //
My code:
my_list = [1,1,2,2,2,3,3,3,3,3,4,4,4,5,5]
my_series = pd.Series(my_list)
values_counted = pd.Series(my_series.value_counts(),name='count')
# other calculated columns left out for SO simplicity
df = pd.concat([values_counted], axis=1).sort_index()
df['tally'] = values_counted * '/'
With the code above I get the following error:
masked_arith_op
result[mask] = op(xrav[mask], y)
numpy.core._exceptions.UFuncTypeError: ufunc 'multiply' did not contain a loop with signature matching types (dtype('<U21'), dtype('<U21')) -> dtype('<U21')
In searching for solutions I found one on SO that said to try:
values_counted * float('/')
But that did not work.
In 'normal' Python outside of Dataframes the following code works:
10 * '/'
and returns
///////////
How can I achieve the same functionality in a Dataframe?
Use lambda function for repeat values, your solution is simplify:
my_list = [1,1,2,2,2,3,3,3,3,3,4,4,4,5,5]
df1 = pd.Series(my_list).value_counts().to_frame('count').sort_index()
df1['tally'] = df1['count'].apply(lambda x: x * '/')
print (df1)
count tally
1 2 //
2 3 ///
3 5 /////
4 3 ///
5 2 //
You can group the series by itself and then aggregate:
new_df = my_series.groupby(my_series).agg(**{"count": "size",
"tally": lambda s: "/" * s.size})
to get
>>> new_df
count tally
1 2 //
2 3 ///
3 5 /////
4 3 ///
5 2 //
Related
In the following picture, I have DataFrame that renders zero after each cycle of operation (the cycle has random length). I want to calculate the average (or perform other operations) for each patch. For example, the average of [0.762, 0.766] alone, and [0.66, 1.37, 2.11, 2.29] alone and so forth till the end of the DataFrame.
So I worked with this data :
random_value
0 0
1 0
2 1
3 2
4 3
5 0
6 4
7 4
8 0
9 1
There is probably a way better solution, but here is what I came with :
def avg_function(df):
avg_list = []
value_list = list(df["random_value"])
temp_list = []
for i in range(len(value_list)):
if value_list[i] == 0:
if temp_list:
avg_list.append(sum(temp_list) / len(temp_list))
temp_list = []
else:
temp_list.append(value_list[i])
if temp_list: # for the last values
avg_list.append(sum(temp_list) / len(temp_list))
return avg_list
test_list = avg_function(df=df)
test_list
[Out] : [2.0, 4.0, 1.0]
Edit: since requested in the comments, here is a way to add the means to the dataframe. I dont know if there is a way to do that with pandas (and there might be!), but I came up with this :
def add_mean(df, mean_list):
temp_mean_list = []
list_index = 0 # will be the index for the value of mean_list
df["random_value_shifted"] = df["random_value"].shift(1).fillna(0)
random_value = list(df["random_value"])
random_value_shifted = list(df["random_value_shifted"])
for i in range(df.shape[0]):
if random_value[i] == 0 and random_value_shifted[i] == 0:
temp_mean_list.append(0)
elif random_value[i] == 0 and random_value_shifted[i] != 0:
temp_mean_list.append(0)
list_index += 1
else:
temp_mean_list.append(mean_list[list_index])
df = df.drop(["random_value_shifted"], axis=1)
df["mean"] = temp_mean_list
return df
df = add_mean(df=df, mean_list=mean_list
Which gave me :
df
[Out] :
random_value mean
0 0 0
1 0 0
2 1 2
3 2 2
4 3 2
5 0 0
6 4 4
7 4 4
8 0 0
9 1 1
I have a pandas data frame which is shown below:
>>> x = [[1,2,3,4,5],[1,2,4,4,3],[2,4,5,6,7]]
>>> columns = ['a','b','c','d','e']
>>> df = pd.DataFrame(data = x, columns = columns)
>>> df
a b c d e
0 1 2 3 4 5
1 1 2 4 4 3
2 2 4 5 6 7
I have an array of objects (conditions) as shown below:
[
{
'header' : 'a',
'condition' : '==',
'values' : [1]
},
{
'header' : 'b',
'condition' : '==',
'values' : [2]
},
...
]
and an assignHeader which is:
assignHeader = decision
now I want to do an operation which builds up all the conditions from the conditions array by looping through it, for example something like this:
pConditions = []
for eachCondition in conditions:
header = eachCondition['header']
values = eachCondition['values']
if eachCondition['condition'] == "==":
pConditions.append(df[header].isin(values))
else:
pConditions.append(~df[header].isin(values))
df[assignHeader ] = and(pConditions)
I was thinking of using all operator in pandas but am unable to crack the right syntax to do so. The list I shared can go big and dynamic and so I want to use this nested approach and check for the equality. Does anyone know a way to do so?
Final Output:
conditons = [df['a']==1,df['b']==2]
>>> df['decision'] = (df['a']==1) & (df['b']==2)
>>> df
a b c d e decision
0 1 2 3 4 5 True
1 1 2 4 4 3 True
2 2 4 5 6 7 False
Here conditions array will be variable. And I want to have a function which takes df, 'newheadernameandconditions` as input and returns the output as shown below:
>>> df
a b c d e decision
0 1 2 3 4 5 True
1 1 2 4 4 3 True
2 2 4 5 6 7 False
where newheadername = 'decision'
I was able to solve the problem using the code shown below. I am not sure if this is kind of fast way of getting things done, but would love to know your inputs in case you have any specific thing to point out.
def andMerging(conditions, mergeHeader, df):
if len(conditions) != 0:
df[mergeHeader] = pd.concat(conditions, axis = 1).all(axis = 1)
return df
where conditions are an array of pd.Series with boolean values.
And conditions are formatted as shown below:
def prepareForConditionMerging(conditionsArray, df):
conditions = []
for prop in conditionsArray:
condition = prop['condition']
values = prop['values']
header = prop['header']
if type(values) == str:
values = [values]
if condition=="==":
conditions.append(df[header].isin(values))
else:
conditions.append(~df[header].isin(values))
# Here we can add more conditions such as greater than less than etc.
return conditions
Hello I am obliged to downgrade Pandas versioon to '0.24.2'
As a result, the function pd.NamedAgg is not recognizable anymore.
import pandas as pd
import numpy as np
agg_cols = ['A', 'B', 'C']
agg_df = df.groupby(agg_cols).agg(
max_foo=pd.NamedAgg(column='Foo', aggfunc=np.max),
min_foo=pd.NamedAgg(column='Foo', aggfunc=np.min)
).reset_index()
Can you help me please change my code to make it compliant with the version 0.24.2??
Thank you a lot.
Sample:
agg_df = df.groupby(agg_cols)['Foo'].agg(
[('max_foo', np.max),('min_foo', np.min)]
).reset_index()
df = pd.DataFrame({
'A':list('a')*6,
'B':[4,5,4,5,5,4],
'C':[7]*6,
'Foo':[1,3,5,7,1,0],
'E':[5,3,6,9,2,4],
'F':list('aaabbb')
})
agg_cols = ['A', 'B', 'C']
agg_df = df.groupby(agg_cols).agg(
max_foo=pd.NamedAgg(column='Foo', aggfunc=np.max),
min_foo=pd.NamedAgg(column='Foo', aggfunc=np.min)
).reset_index()
print (agg_df)
A B C max_foo min_foo
0 a 4 7 5 0
1 a 5 7 7 1
Because there is only one column Foo for processing add column Foo after groupby and pass tuples with new columns names with aggregate functions:
agg_df = df.groupby(agg_cols)['Foo'].agg(
[('max_foo', np.max),('min_foo', np.min)]
).reset_index()
print (agg_df)
A B C max_foo min_foo
0 a 4 7 5 0
1 a 5 7 7 1
Another idea is pass dictionary of lists of aggregate functions:
agg_df = df.groupby(agg_cols).agg({'Foo':['max', 'min']})
agg_df.columns = [f'{b}_{a}' for a, b in agg_df.columns]
agg_df = agg_df.reset_index()
print (agg_df)
A B C max_foo min_foo
0 a 4 7 5 0
1 a 5 7 7 1
Let's say I have a .CSV which has three columns: tidytext, location, vader_senti
I was already able to get the amount of *positive, neutral and negative text instead of word* pero country using the following code:
data_vis = pd.read_csv(r"csviamcrpreprocessed.csv", usecols=fields)
def print_sentiment_scores(text):
vadersenti = analyser.polarity_scores(str(text))
return pd.Series([vadersenti['pos'], vadersenti['neg'], vadersenti['neu'], vadersenti['compound']])
data_vis[['vadersenti_pos', 'vadersenti_neg', 'vadersenti_neu', 'vadersenti_compound']] = data_vis['tidytext'].apply(print_sentiment_scores)
data_vis['vader_senti'] = 'neutral'
data_vis.loc[data_vis['vadersenti_compound'] > 0.3 , 'vader_senti'] = 'positive'
data_vis.loc[data_vis['vadersenti_compound'] < 0.23 , 'vader_senti'] = 'negative'
data_vis['vader_possentiment'] = 0
data_vis.loc[data_vis['vadersenti_compound'] > 0.3 , 'vader_possentiment'] = 1
data_vis['vader_negsentiment'] = 0
data_vis.loc[data_vis['vadersenti_compound'] <0.23 , 'vader_negsentiment'] = 1
data_vis['vader_neusentiment'] = 0
data_vis.loc[(data_vis['vadersenti_compound'] <=0.3) & (data_vis['vadersenti_compound'] >=0.23) , 'vader_neusentiment'] = 1
sentimentbylocation = data_vis.groupby(["Location"])['vader_senti'].value_counts()
sentimentbylocation
sentimentbylocation gives me the following results:
Location vader_senti
Afghanistan negative 151
positive 25
neutral 2
Albania negative 6
positive 1
Algeria negative 116
positive 13
neutral 4
TO GET THE MOST COMMON POSITIVE WORDS, I USED THIS CODE:
def process_text(text):
tokens = []
for line in text:
toks = tokenizer.tokenize(line)
toks = [t.lower() for t in toks if t.lower() not in stopwords_list]
tokens.extend(toks)
return tokens
tokenizer=TweetTokenizer()
punct = list(string.punctuation)
stopwords_list = stopwords.words('english') + punct + ['rt','via','...','…','’','—','—:',"‚","â"]
pos_lines = list(data_vis[data_vis.vader_senti == 'positive'].tidytext)
pos_tokens = process_text(pos_lines)
pos_freq = nltk.FreqDist(pos_tokens)
pos_freq.most_common()
Running this will give me the most common words and the number of times they appeared, such as
[(good, 1212),
(amazing, 123)
However, what I want to see is how many of these positive words appeared in a country.
For example:
I have a sample CSV here: https://drive.google.com/file/d/112k-6VLB3UyljFFUbeo7KhulcrMedR-l/view?usp=sharing
Create a column for each most_common word, then do a groupby location and use agg to apply a sum for each count:
words = [i[0] for i in pos_freq.most_common()]
# lowering all cases in tidytext
data_vis.tidytext = data_vis.tidytext.str.lower()
for i in words:
data_vis[i] = data_vis.tidytext.str.count(i)
funs = {i: 'sum' for i in words}
grouped = data_vis.groupby('Location').agg(funs)
Based on the example from the CSV and using most_common as ['good', 'amazing'] the result would be:
grouped
# good amazing
# Location
# Australia 0 1
# Belgium 6 4
# Japan 2 1
# Thailand 2 0
# United States 1 0
I have a huge Pandas DataFrame that I need to write away to a format that RankLib can understand. Example with a target, a query ID and 3 features is this:
5 qid:4 1:12 2:0.6 3:13
1 qid:4 1:8 2:0.4 3:11
I have written my own function that iterates over the rows and writes them away like this:
data_file = open(filename, 'w')
for index, row in data.iterrows():
line = str(row['score'])
line += ' qid:'+str(row['srch_id'])
counter = 0
for feature in feature_columns:
counter += 1
line += ' '+str(counter)+':'+str(row[feature])
data_file.write(line+'\n')
data_file.close()
Since I have about 200 features and 5m rows this is obviously very slow. Is there a better approach using the I/O of Pandas itself?
you can do it this way:
Data:
In [155]: df
Out[155]:
f1 f2 f3 score srch_id
0 12 0.6 13 5 4
1 8 0.4 11 1 4
2 11 0.7 14 2 10
In [156]: df.dtypes
Out[156]:
f1 int64
f2 float64
f3 int64
score object
srch_id int64
dtype: object
Solution:
feature_columns = ['f1','f2','f3']
cols2id = {col:str(i+1) for i,col in enumerate(feature_columns)}
def f(x):
if x.name in feature_columns:
return cols2id[x.name] + ':' + x.astype(str)
elif x.name == 'srch_id':
return 'quid:' + x.astype(str)
else:
return x
(df.apply(lambda x: f(x))[['score','srch_id'] + feature_columns]
.to_csv('d:/temp/out.csv', sep=' ', index=False, header=None)
)
out.csv:
5 quid:4 1:12 2:0.6 3:13
1 quid:4 1:8 2:0.4 3:11
2 quid:10 1:11 2:0.7 3:14
cols2id helper dict:
In [158]: cols2id
Out[158]: {'f1': '1', 'f2': '2', 'f3': '3'}