If I have this length.csv file content:
May I know how can I use pandas plot dot graph base on this xy and yx?
import pandas as pd
df = pd.read_csv('C:\\path\to\folder\length.csv')
Now if you print df, you will get the following
df.plot(x='yx', y='xy', kind='scatter')
You can change your plot type to different types like line, bar etc.
Refer to https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html
You can easily use matplotlib. The plot method in Pandas is a wrapper for matplotlib.
If you wish to use Pandas, you can do it as such:
import pandas as pd
df = pd.read_csv('length.csv')
df.plot(x='xy', y='yx')
If you decide to go ahead with matplotlib, you can do as follows:
import matplotlib.pyplot as plt
import pandas as pd
%matplotlib inline # Include this line only if on a notebook (like Jupyter or Colab)
df = pd.read_csv('length.csv')
plt.plot(df['xy'], df['yx'])
plt.xlabel('xy')
plt.ylabel('yx')
plt.title('xy vs yx Plot')
plt.show()
Related
I am attempting to create a histogram using seaborn and census data that displays 3 subplots for age composition, and I have the data grouped the way that I would like it, but I am struggling to turn that into a histogram.
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
filename = "/scratch/%s_class_root/%s_class/materials/data/pums_short.csv.gz"
acs = pd.read_csv(filename)
R65_agg = acs.groupby(["R65", "PUMA"])["HINCP"]
R65_meds = R65_agg.agg(np.median).unstack()
R65_f = R65_meds.dropna()
R65_f = R65_meds.reset_index(drop = True)
I was expecting this code to give me data that I could plug into a histogram but instead of being distinct subplots, the "0.0, 1.0, 2,0" in the final variable just get added together when I apply the .describe() function. Any advice for how I can convert this into a form that's readable with the sns.histplot() function?
I have a scatter plot im working with and for some reason im not seeing all the x values on my graph
#%%
from pandas import DataFrame, read_csv
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
file = r"re2.csv"
df = pd.read_csv(file)
#sns.set(rc={'figure.figsize':(11.7,8.27)})
g = sns.FacetGrid(df, col='city')
g.map(plt.scatter, 'type', 'price').add_legend()
This is an image of a small subset of my plots, you can see that Res is displaying, the middle bar should be displaying Con and the last would be Mlt. These are all defined in the type column from my data set but are not displaying.
Any clue how to fix?
Python is doing what you tell it to do. Just pick different features, presumably things that make more sense for plotting, if you want to generate a more interesting plots. See this generic example below.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme(style="darkgrid")
tips = sns.load_dataset("tips")
sns.relplot(x="total_bill", y="tip", hue="smoker", data=tips);
Personally, I like plotly plots, which are dynamic, more than I like seaborn plots.
https://plotly.com/python/line-and-scatter/
I am trying to make a Box and Whisker plot on my dataset that looks something like this -
& the chart I'm trying to make
My current lines of code are below -
import seaborn as sns
import matplotlib.pyplot as plt
d = df3.boxplot(column = ['Northern California','New York','Kansas','Texas'], by = 'Banner')
d
Thank you
I've recreated a dummy version of your dataset:
import numpy as np
import pandas as pd
dictionary = {'Banner':['Type1']*10+['Type2']*10,
'Northen_californina':np.random.rand(20),
'Texas':np.random.rand(20)}
df = pd.DataFrame(dictionary)
What you need is to melt your dataframe (unpivot) in orther to have the information of geographical zone stored in a column and not as column name. You can use pandas.melt method and specify all the columns you want to put in your boxplot in the value_vars argument.
With my dummy dataset you can do this:
df = pd.melt(df,id_vars=['Banner'],value_vars=['Northen_californina','Texas'],
var_name='zone', value_name='amount')
Now you can apply a boxplot using the hue argument:
import seaborn as sns
import matplotlib.pyplot as plt
plt.figure(figsize=(9,9)) #for a bigger image
sns.boxplot(x="Banner", y="amount", hue="zone", data=df, palette="Set1")
I am trying to create a heatmap with dendrograms on Python using Seaborn and I have a csv file with about 900 rows. I'm importing the file as a pandas dataframe and attempting to plot that but a large number of the rows are not being represented in the heatmap. What am I doing wrong?
This is the code I have right now. But the heatmap only represents about 49 rows.
Here is an image of the clustermap I've obtained but it is not displaying all of my data.
import seaborn as sns
import pandas as pd
from matplotlib import pyplot as plt
# Data set
df = pd.read_csv('diff_exp_gene.csv', index_col = 0)
# Default plot
sns.clustermap(df, cmap = 'RdBu', row_cluster=True, col_cluster=True)
plt.show()
Thank you.
An alternative approach would be to use imshow in matpltlib. I'm not exactly sure what your question is but I demonstrate a way to graph points on a plane from csv file
import numpy as np
import matplotlib.pyplot as plt
import csv
infile = open('diff_exp_gene.csv')
df = csv.DictReader(in_file)
temp = np.zeros((128,128), dtype = int)
for row in data:
if row['TYPE'] == types:
temp[int(row['Y'])][int(row['X'])] = temp[int(row['Y'])][int(row['X'])] + 1
plt.imshow(temp, cmap = 'hot', origin = 'lower')
plt.show()
As far as I know, keywords that apply to seaborn heatmaps also apply to clustermap, as the sns.clustermap passes to the sns.heatmap. In that case, all you need to do in your example is to set yticklabels=True as a keyword argument in sns.clustermap(). That will make all of the 900 rows appear.
By default, it is set as "auto" to avoid overlap. The same applies to the xticklabels. See more here: https://seaborn.pydata.org/generated/seaborn.heatmap.html
i want to convert that dataframe
into this dataframe and plot a matplotlib graph using date along x axis
changed dataframe
Use df.T.plot(kind='bar'):
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame.from_csv('./housing_price_index_2010-11_100.csv')
df.T.plot(kind='bar')
plt.show()
you can also assign the transpose to a new variable and plot that (what you asked in the comment):
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame.from_csv('./housing_price_index_2010-11_100.csv')
df_transposed = df.T
df_transposed.plot(kind='bar')
plt.show()
both result the same: