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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.
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
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
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
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
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