繁體中文
English Indonesia Türkçe Deutsch Español Português Pусский Français Italiano Nederlands Polski العربية ไทย Tiếng Việt 繁體中文 简体中文 日本語 한국어
codingboo's profile picture. Learning Data Science and Data Analytics!

Elena Chen

@codingboo

Learning Data Science and Data Analytics!

148貼文 4位跟隨者 9個跟隨中
你可能會喜歡
codingboo's profile picture.
Elena Chen
 @codingboo
2023年5月6日

#Day15 of #DataAnalytics #Seaborns Place data in matrix form by .pivot_table() .heatmap to plot data in color-encoded matrices. annot=True for annotation of the values to be presented on the grid. cmap to change color variation VS .clustermap data grouped based on similarity

codingboo's tweet image. #Day15 of #DataAnalytics #Seaborns Place data in matrix form by .pivot_table() .heatmap to plot data in color-encoded matrices. annot=True for annotation of the values to be presented on the grid. cmap to change color variation VS .clustermap data grouped based on similarity
codingboo's tweet image. #Day15 of #DataAnalytics #Seaborns Place data in matrix form by .pivot_table() .heatmap to plot data in color-encoded matrices. annot=True for annotation of the values to be presented on the grid. cmap to change color variation VS .clustermap data grouped based on similarity
codingboo's tweet image. #Day15 of #DataAnalytics #Seaborns Place data in matrix form by .pivot_table() .heatmap to plot data in color-encoded matrices. annot=True for annotation of the values to be presented on the grid. cmap to change color variation VS .clustermap data grouped based on similarity
0
0
1
0
23
codingboo's profile picture.
Elena Chen
 @codingboo
2023年5月4日

#Day14 of #DataAnalytics #Seaborns kdeplot - kernel density estimation. Idea is to replace each data point (represented by dashmark in rugplot) with a small Gaussian (Normal) distribution centered around that value, then summing the Gaussians for smooth estimate of the distributi

codingboo's tweet image. #Day14 of #DataAnalytics #Seaborns kdeplot - kernel density estimation. Idea is to replace each data point (represented by dashmark in rugplot) with a small Gaussian (Normal) distribution centered around that value, then summing the Gaussians for smooth estimate of the distributi
codingboo's tweet image. #Day14 of #DataAnalytics #Seaborns kdeplot - kernel density estimation. Idea is to replace each data point (represented by dashmark in rugplot) with a small Gaussian (Normal) distribution centered around that value, then summing the Gaussians for smooth estimate of the distributi
1
0
1
0
39
codingboo's profile picture.
Elena Chen
 @codingboo
2023年4月28日

#Day11 of #DataAnalytics I'm struggling with #Matplotlib because my kernel keeps restarting/dying whenever I try to import matplotlib... this was the same problem I faced the previous time when I was learning this too...

0
0
0
0
7
codingboo's profile picture.
Elena Chen
 @codingboo
2023年4月27日

#Day10 of #DataAnalytics Started #Matplotlib visualization tool for Python! View: matplotlib.org/2.0.2/gallery.… to see the whole list of figures that can be done + source code (eg statistical plots & scientific figures) import matplotlib.pyplot as plt %matplotlib inline plt.plot()

codingboo's tweet image. #Day10 of #DataAnalytics Started #Matplotlib visualization tool for Python! View: matplotlib.org/2.0.2/gallery.… to see the whole list of figures that can be done + source code (eg statistical plots & scientific figures) import matplotlib.pyplot as plt %matplotlib inline plt.plot()
0
0
0
0
140
codingboo's profile picture.
Elena Chen
 @codingboo
2023年4月26日

#Day9 of #DataAnalytics Finished a last section of learning #Pandas, and did extracting data with: - str.contain(' ', case=False) to make it case-insensitive - .head(n) to get the first n rows, usually paired with .value_counts - len(df[’col2’].unique()) / df[’col2’].nunique()

codingboo's tweet image. #Day9 of #DataAnalytics Finished a last section of learning #Pandas, and did extracting data with: - str.contain(' ', case=False) to make it case-insensitive - .head(n) to get the first n rows, usually paired with .value_counts - len(df[’col2’].unique()) / df[’col2’].nunique()
codingboo's tweet image. #Day9 of #DataAnalytics Finished a last section of learning #Pandas, and did extracting data with: - str.contain(' ', case=False) to make it case-insensitive - .head(n) to get the first n rows, usually paired with .value_counts - len(df[’col2’].unique()) / df[’col2’].nunique()
codingboo's tweet image. #Day9 of #DataAnalytics Finished a last section of learning #Pandas, and did extracting data with: - str.contain(' ', case=False) to make it case-insensitive - .head(n) to get the first n rows, usually paired with .value_counts - len(df[’col2’].unique()) / df[’col2’].nunique()
codingboo's tweet image. #Day9 of #DataAnalytics Finished a last section of learning #Pandas, and did extracting data with: - str.contain(' ', case=False) to make it case-insensitive - .head(n) to get the first n rows, usually paired with .value_counts - len(df[’col2’].unique()) / df[’col2’].nunique()
0
0
0
0
6

United States 趨勢

你可能會喜歡

Something went wrong.


Something went wrong.