seaborn

问题 I'm making automatic scatterplot charting with regression from my dataframe example I want to make correlation between Column2 to Column3 and Column2 to Column4 in separate scatter plot group by Column1. For example, there will be 3 scatter plot of Column2 to Column3 each with the title of A, B, and C For the plotting I'm using pandas scatter plot for example: df.groupby('Column1').plot.scatter(x='Column2',y='Column3') Scatter plot return exactly 3 plot, but I want to know how to add chart

问题 Is there a way to automatically add contour (iso-z) lines to a heatmap with concrete x and y values? Please consider the official seaborn flights dataset: import seaborn as sns flights = sns.load_dataset("flights") flights = flights.pivot("month", "year", "passengers") sns.heatmap(flights, annot=True, fmt='d') I imagine the step-like lines to look something like shown below (lhs), indicating thresholds (here 200 and 400). They do not need to be interpolated or smoothed in any way, although

问题 Is there a way to automatically add contour (iso-z) lines to a heatmap with concrete x and y values? Please consider the official seaborn flights dataset: import seaborn as sns flights = sns.load_dataset("flights") flights = flights.pivot("month", "year", "passengers") sns.heatmap(flights, annot=True, fmt='d') I imagine the step-like lines to look something like shown below (lhs), indicating thresholds (here 200 and 400). They do not need to be interpolated or smoothed in any way, although

问题 Is there a way to automatically add contour (iso-z) lines to a heatmap with concrete x and y values? Please consider the official seaborn flights dataset: import seaborn as sns flights = sns.load_dataset("flights") flights = flights.pivot("month", "year", "passengers") sns.heatmap(flights, annot=True, fmt='d') I imagine the step-like lines to look something like shown below (lhs), indicating thresholds (here 200 and 400). They do not need to be interpolated or smoothed in any way, although

问题 I'm looking for a way to plot a distribution histogram, with the y-axis representing the total number of items for each bin (and not just the count). Example on the charts below: On the left, there are 55 agencies who sold between 20-30 houses On the right, the agencies having sold between 20-30 houses represent 1100 houses sold It's not as trivial as it looks because one can't simply multiply each bin's count by the bin's value (maybe in the 20-30 bin, there are 54 agencies who sold 21 are 1

问题 I'm looking for a way to plot a distribution histogram, with the y-axis representing the total number of items for each bin (and not just the count). Example on the charts below: On the left, there are 55 agencies who sold between 20-30 houses On the right, the agencies having sold between 20-30 houses represent 1100 houses sold It's not as trivial as it looks because one can't simply multiply each bin's count by the bin's value (maybe in the 20-30 bin, there are 54 agencies who sold 21 are 1

问题 I'm using stripplot on seaborn to show energies of d orbitals in a series of metallic centers. Here is the dataframe: dxy dyz dz2 dxz dx2-y2 Fe -0.25336 -0.24661 -0.22991 -0.07644 -0.16229 Co -0.38294 -0.38050 -0.34952 -0.21271 -0.27173 Ni -0.47550 -0.47504 -0.46817 -0.44385 -0.45632 And using this code I'm quite close to what I want (resulting image below): plt.figure(figsize=(3, 7)) sns.stripplot(x=df.index, y="dxy", data=df, jitter=False, dodge=True, size=44, marker="_", linewidth=2) sns

问题 I'm using stripplot on seaborn to show energies of d orbitals in a series of metallic centers. Here is the dataframe: dxy dyz dz2 dxz dx2-y2 Fe -0.25336 -0.24661 -0.22991 -0.07644 -0.16229 Co -0.38294 -0.38050 -0.34952 -0.21271 -0.27173 Ni -0.47550 -0.47504 -0.46817 -0.44385 -0.45632 And using this code I'm quite close to what I want (resulting image below): plt.figure(figsize=(3, 7)) sns.stripplot(x=df.index, y="dxy", data=df, jitter=False, dodge=True, size=44, marker="_", linewidth=2) sns

问题 I have a dataframe df that looks like this: df.head() id feedback nlp_model similarity_score 0xijh4 1 tfidf 0.36 0sdnj7 -1 lda 0.89 kjh458 1 doc2vec 0.78 .... I want to plot similairty_score versus feedback in a boxplot form using seaborn for each of the unique values in the model column: tfidf , lda , doc2vec . My code for this is as follows: fig, ax = plt.subplots(figsize=(10,8)) ax = sns.boxplot(x="feedback", y="similarity_score", data=df[df.nlp_model=='tfidf']) ax = sns.swarmplot(x=

问题 I have 2 data tables with the dimensions 4x25 . Each table is from a different point in time, but has exactly the same meta data, in essence the same column and row headers. Given the large number of columns, I thought it best to represent this using a heatmap using the seaborn library for Python . However, I need to include both tables in the same plot. I am able to create a single heatmap representing a single data table as so. df = pd.DataFrame(raw_data) ax = sns.heatmap(df) ax.set