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direction | tau | |
---|---|---|
1.0 | 44.17309441579963 | |
1.0 | 39.13313136222197 | |
1.0 | 2.229445925263579 | |
1.0 | 45.51885740055968 | |
1.0 | 32.46369490822559 | |
0.0 | 34.32693114812909 | |
1.0 | 29.851265008411353 | |
1.0 | 22.108872716835183 | |
0.0 | 39.32300084317585 |
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import pandas as pd | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import cps | |
import seaborn as sns | |
sns.set_style('white') | |
plt.ion() | |
# This is a quick hack that gets the job done :/ | |
def repulsion(arr): |
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from dplython import * | |
diamonds >> group_by(X.cut) >> summarize(mean_x = np.mean(X.x), var_y = np.var(X.y)) | |
diamonds >> group_by(X.cut) >> mutate(mean_x = np.mean(X.x), var_y = np.var(X.y)) |
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# R | |
# 1) Easily create multiple summaries of multiple column. | |
diamonds %>% | |
group_by(cut) %>% | |
summarize(mean_x = mean(x), var_xy = var(x * y)) | |
# 2) Easily put multiple summaries back into the original data frame | |
diamonds %>% | |
group_by(cut) %>% |
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import pandas as pd | |
from ggplot import diamonds | |
# 1) How do I easily create multiple summaries of multiple columns? | |
( | |
diamonds | |
.groupby('cut') | |
.agg(??) # Equivalent of summarize(mean_x = mean(x), var_xy = var(xy)) | |
) |