In order to visualize data from a Pandas DataFrame, you must extract each Series and often concatenate them together into the right format. Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas DataFrames.Doing sophisticated statistical visualization is possible, but often requires a lot of boilerplate code. Matplotlib's API is relatively low level.It was based off of MATLAB circa 1999, and this often shows. Prior to version 2.0, Matplotlib's defaults are not exactly the best choices.There are several valid complaints about Matplotlib that often come up: ,Alpha Food Corps,65,35720,0.Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired. Sns.lineplot('date', 'values', data=data, hue='cats')ĬSV Sample: date,dealer,threshold,quantity,price,protein_type,destination Optionally, swap the order of dealer and proteinĭata = dfl.This will product one column of 4 figures, selected first for dealer and then protein_type.G.map(sns.lineplot, 'date', 'values').add_legend() Option 2: Four Plots seaborn.FacetGrid g = sns.FacetGrid(data=dfl, col='dealer', row='protein_type', hue='cats', height=5, aspect=1.5) Even with only 'price' and 'expected_price', 'dealer' can't be determined.Sns.lineplot('date', 'values', data=data, hue='cats', style='dealer') The 'dealer' data is to similar to be differentiated (price collusion anyone?).# read the data in and parse the date column and set threshold as a strĭf = pd.read_csv('data/so_data/ 63239708/mydf.csv', parse_dates=)ĭf = df.price*76/df.thresholdĭf.threshold = df.threshold.astype('category')ĭf = df.set_index()ĭfl = df.drop(columns=).stack().reset_index().rename(columns=) date, values and cats) for one dealer, one threshold, and one protein_type. To clearly present the data, each plot should contain only 3 dimensions of data (e.g.There is a lot of unexplained information in the plots and they do not clearly present the data.This option was implemented after seeing the results of Option 1.Here is the example possible line chart I want to have: I want either line chart or scatter plot where x-axis shows monthly time series while y-axis shows price of each different protein_type on different threshold value for each different dealer along monthly time series. Here is my attempt for making plot: def scatterplot(x_data, y_data, x_label, y_label, title):Īx.scatter(x_data, y_data, s = 30, color = '#539caf', alpha = 0.75) Also in my data, date is not continuous so I assume I could make plot of monthly time series line chart. Pv_df.groupby().unstack().reset_index()īut above attempt is still not working.
pv_df= pd.pivot_table(mydf, index=, columns=,values=)
I also tried pandas/pivot_table for aggregating my data but it is still not representing plotting data.
SEABORN SCATTER PLOT TIME HOW TO
Can anyone suggest me or correct me how to get this right? I don't know what's best way of dealing with this time series. Newdf= g.apply(lambda x: pd.Series()])).unstack()īut above attempt is not working because I want to have plotting data for each dealer's market purchase price on different protein_type with different threshold along the daily time series. Can anyone point me out how to make this right to get desired plot? import pandas as pd I bet the my way of aggregating plotting data is not correct. Here is my current attempt to aggregate my data for making plotting data but it is not giving my right plot. Here is example product data that I used wheres I want to see each dealer's price trend on different protein type with respect to threshold. I tried of using pandas.pivot_table, groupby for shaping plotting data, but couldn't get desired plot that I want to make. My data is product sales prices time series data, I want to see each product owner' price trend on different market threshold along times. I want to make time series scatter plot for my time series data, where my data has categorical columns which needs to be aggregated by group to make plotting data first, then make scatter plot either using seaborn or matplotlib.
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