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Results tagged with pandas
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user 69822
pandas is a python library for Panel Data manipulation and analysis, e.g. multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance.
12
votes
Accepted
How to use SimpleImputer Class to replace missing values with mean values using Python?
Your error is due to using Simple Imputer's fit and fit_transform on a numpy array. Here's how i used it on a Dataframe
imr = Imputer(missing_values='NaN', strategy='median', axis=0)
imr = imr.fit(da …
- 2,094
7
votes
Accepted
Why is pandas corr() deleting columns?
Pearson's correlation is the default correlation used with Pandas corr method.
Categorical features ( not numerical ) are ignored during this process due to their nature of not being continuous. …
- 2,094
6
votes
Accepted
Obtaining consistent one-hot encoding of train / production data
Use sklearn.preprocessing.OneHotEncoder and transfer the one-hot encoding to your web-service ( i'm guessing that's how you're using the model for inference ) via sklearn.pipeline.Pipeline. The pipeli …
- 2,094
2
votes
Problem with sort by and group by in pandas
All you need is a groupby operation + aggregation on the min/max values.
df.groupby('id').agg(('min','max'))['date_column']
The output should be like this : different dataframe with each line cont …
- 2,094
1
vote
Accepted
Similarity of XGBoost models?
No, they won't have neither the same performance nor the same architecture if you were to try to visualize it. An XGBoost with 100 n_estimators and a learning rate of 0.1 is a 100 trees grown sequenti …
- 2,094
1
vote
The actual results and results from pickle files are not matching in pandas for DBSCAN clust...
Without looking at anything else :
pred_val = pickle_model.fit_predict(test[['HD','MC_encoded']])
You're training your pickle_model on your test_data by using fit_predict() method. Start by replaci …
- 2,094
0
votes
Accepted
find the difference between two columns in specific rows
for i in range (99,len(df),200):
try:
df1=df.loc[i+200,'acctimestamp'] - df.loc[i+200,'gyrtimestamp']
print(df1)
except:
print('End')
Is what you're looking for? Step i …
- 2,094