#!/usr/bin/env python
# coding: utf-8
# # Heatmap using seabron
# In[1]:
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# In[2]:
data_2d = np.linspace(1,5 ,12).reshape(4,3)
data_2d
# In[3]:
sns.heatmap(data_2d)
heatmap show only numeric valuse
# In[4]:
data=pd.read_csv("who is responsible for global warming.csv")
data.head(2)
# In[5]:
data1=data.drop(["Country Code","Indicator Name","Indicator Code"],axis=1).set_index("Country Name")
# In[6]:
plt.figure(figsize=(16,9))
sns.heatmap(data1)
sns.heatmap(
data,
vmin=None,
vmax=None,
cmap=None,
center=None,
robust=False,
annot=None,
fmt='.2g',
annot_kws=None,
linewidths=0,
linecolor='white',
cbar=True,
cbar_kws=None,
cbar_ax=None,
square=False,
xticklabels='auto',
yticklabels='auto',
mask=None,
ax=None,
**kwargs,
)
Docstring:
Plot rectangular data as a color-encoded matrix.
This is an Axes-level function and will draw the heatmap into the
currently-active Axes if none is provided to the ``ax`` argument. Part of
this Axes space will be taken and used to plot a colormap, unless ``cbar``
is False or a separate Axes is provided to ``cbar_ax``.
Parameters
----------
data : rectangular dataset
2D dataset that can be coerced into an ndarray. If a Pandas DataFrame
is provided, the index/column information will be used to label the
columns and rows.
vmin, vmax : floats, optional
Values to anchor the colormap, otherwise they are inferred from the
data and other keyword arguments.
cmap : matplotlib colormap name or object, or list of colors, optional
The mapping from data values to color space. If not provided, the
default will depend on whether ``center`` is set.
center : float, optional
The value at which to center the colormap when plotting divergant data.
Using this parameter will change the default ``cmap`` if none is
specified.
robust : bool, optional
If True and ``vmin`` or ``vmax`` are absent, the colormap range is
computed with robust quantiles instead of the extreme values.
annot : bool or rectangular dataset, optional
If True, write the data value in each cell. If an array-like with the
same shape as ``data``, then use this to annotate the heatmap instead
of the data. Note that DataFrames will match on position, not index.
fmt : string, optional
String formatting code to use when adding annotations.
annot_kws : dict of key, value mappings, optional
Keyword arguments for ``ax.text`` when ``annot`` is True.
linewidths : float, optional
Width of the lines that will divide each cell.
linecolor : color, optional
Color of the lines that will divide each cell.
cbar : boolean, optional
Whether to draw a colorbar.
cbar_kws : dict of key, value mappings, optional
Keyword arguments for `fig.colorbar`.
cbar_ax : matplotlib Axes, optional
Axes in which to draw the colorbar, otherwise take space from the
main Axes.
square : boolean, optional
If True, set the Axes aspect to "equal" so each cell will be
square-shaped.
xticklabels, yticklabels : "auto", bool, list-like, or int, optional
If True, plot the column names of the dataframe. If False, don't plot
the column names. If list-like, plot these alternate labels as the
xticklabels. If an integer, use the column names but plot only every
n label. If "auto", try to densely plot non-overlapping labels.
mask : boolean array or DataFrame, optional
If passed, data will not be shown in cells where ``mask`` is True.
Cells with missing values are automatically masked.
ax : matplotlib Axes, optional
Axes in which to draw the plot, otherwise use the currently-active
Axes.
kwargs : other keyword arguments
All other keyword arguments are passed to
:func:`matplotlib.axes.Axes.pcolormesh`.
Returns
-------
ax : matplotlib Axes
Axes object with the heatmap.
See also
--------
clustermap : Plot a matrix using hierachical clustering to arrange the
rows and columns.
Examples
--------
Plot a heatmap for a numpy array:
.. plot::
:context: close-figs
>>> import numpy as np; np.random.seed(0)
>>> import seaborn as sns; sns.set()
>>> uniform_data = np.random.rand(10, 12)
>>> ax = sns.heatmap(uniform_data)
Change the limits of the colormap:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(uniform_data, vmin=0, vmax=1)
Plot a heatmap for data centered on 0 with a diverging colormap:
.. plot::
:context: close-figs
>>> normal_data = np.random.randn(10, 12)
>>> ax = sns.heatmap(normal_data, center=0)
Plot a dataframe with meaningful row and column labels:
.. plot::
:context: close-figs
>>> flights = sns.load_dataset("flights")
>>> flights = flights.pivot("month", "year", "passengers")
>>> ax = sns.heatmap(flights)
Annotate each cell with the numeric value using integer formatting:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, annot=True, fmt="d")
Add lines between each cell:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, linewidths=.5)
Use a different colormap:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, cmap="YlGnBu")
Center the colormap at a specific value:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, center=flights.loc["January", 1955])
Plot every other column label and don't plot row labels:
.. plot::
:context: close-figs
>>> data = np.random.randn(50, 20)
>>> ax = sns.heatmap(data, xticklabels=2, yticklabels=False)
Don't draw a colorbar:
.. plot::
:context: close-figs
>>> ax = sns.heatmap(flights, cbar=False)
Use different axes for the colorbar:
.. plot::
:context: close-figs
>>> grid_kws = {"height_ratios": (.9, .05), "hspace": .3}
>>> f, (ax, cbar_ax) = plt.subplots(2, gridspec_kw=grid_kws)
>>> ax = sns.heatmap(flights, ax=ax,
... cbar_ax=cbar_ax,
... cbar_kws={"orientation": "horizontal"})
Use a mask to plot only part of a matrix
.. plot::
:context: close-figs
>>> corr = np.corrcoef(np.random.randn(10, 200))
>>> mask = np.zeros_like(corr)
>>> mask[np.triu_indices_from(mask)] = True
>>> with sns.axes_style("white"):
... f, ax = plt.subplots(figsize=(7, 5))
... ax = sns.heatmap(corr, mask=mask, vmax=.3, square=True)sns.heatmap(
data,
vmin=None,
vmax=None,
cmap=None,
center=None,
robust=False,
annot=None,
fmt='.2g',
annot_kws=None,
linewidths=0,
linecolor='white',
cbar=True,
cbar_kws=None,
cbar_ax=None,
square=False,
xticklabels='auto',
yticklabels='auto',
mask=None,
ax=None,
**kwargs,
)
# In[7]:
plt.figure(figsize=(16,9))
sns.heatmap(data1, vmin=0,vmax=20)
# In[8]:
"""
cmap valuse= supported values are 'Accent', 'Accent_r', 'Blues',
'Blues_r', 'BrBG', 'BrBG_r', 'BuGn', 'BuGn_r', 'BuPu', 'BuPu_r',
'CMRmap', 'CMRmap_r', 'Dark2', 'Dark2_r', 'GnBu', 'GnBu_r', 'Greens',
'Greens_r', 'Greys', 'Greys_r', 'OrRd', 'OrRd_r', 'Oranges', 'Oranges_r',
'PRGn', 'PRGn_r', 'Paired', 'Paired_r', 'Pastel1', 'Pastel1_r', 'Pastel2',
'Pastel2_r', 'PiYG', 'PiYG_r', 'PuBu', 'PuBuGn', 'PuBuGn_r', 'PuBu_r', 'PuOr',
'PuOr_r', 'PuRd', 'PuRd_r', 'Purples', 'Purples_r', 'RdBu', 'RdBu_r', 'RdGy',
'RdGy_r', 'RdPu', 'RdPu_r', 'RdYlBu', 'RdYlBu_r', 'RdYlGn', 'RdYlGn_r', 'Reds',
'Reds_r', 'Set1', 'Set1_r', 'Set2', 'Set2_r', 'Set3', 'Set3_r', 'Spectral', 'Spectral_r',
'Wistia', 'Wistia_r', 'YlGn', 'YlGnBu', 'YlGnBu_r', 'YlGn_r', 'YlOrBr', 'YlOrBr_r', 'YlOrRd',
'YlOrRd_r', 'afmhot', 'afmhot_r', 'autumn', 'autumn_r', 'binary', 'binary_r', 'bone', 'bone_r',
'brg', 'brg_r', 'bwr', 'bwr_r', 'cividis', 'cividis_r', 'cool', 'cool_r', 'coolwarm',
'coolwarm_r', 'copper', 'copper_r', 'cubehelix', 'cubehelix_r', 'flag', 'flag_r',
'gist_earth', 'gist_earth_r', 'gist_gray', 'gist_gray_r', 'gist_heat', 'gist_heat_r',
'gist_ncar', 'gist_ncar_r', 'gist_rainbow', 'gist_rainbow_r', 'gist_stern', 'gist_stern_r',
'gist_yarg', 'gist_yarg_r', 'gnuplot', 'gnuplot2', 'gnuplot2_r', 'gnuplot_r', 'gray',
'gray_r', 'hot', 'hot_r', 'hsv', 'hsv_r', 'icefire', 'icefire_r', 'inferno', 'inferno_r',
'jet', 'jet_r', 'magma', 'magma_r', 'mako', 'mako_r', 'nipy_spectral', 'nipy_spectral_r',
'ocean', 'ocean_r', 'pink', 'pink_r', 'plasma', 'plasma_r', 'prism', 'prism_r', 'rainbow',
'rainbow_r', 'rocket', 'rocket_r', 'seismic', 'seismic_r', 'spring', 'spring_r', 'summer',
'summer_r', 'tab10', 'tab10_r', 'tab20', 'tab20_r', 'tab20b', 'tab20b_r', 'tab20c', 'tab20c_r',
'terrain', 'terrain_r', 'turbo', 'turbo_r', 'twilight', 'twilight_r', 'twilight_shifted',
'twilight_shifted_r', 'viridis', 'viridis_r', 'vlag', 'vlag_r', 'winter', 'winter_r'
"""
plt.figure(figsize=(16,9))
sns.heatmap(data1, cmap="coolwarm")
# In[9]:
plt.figure(figsize=(16,9))
sns.heatmap(data1, cmap="coolwarm",annot=True)
# In[10]:
data_2d
# In[11]:
arr_2d=np.array([[1,2,3],[4,5,6],[7,8,9],[1,5,9]])
arr_2d
# In[12]:
sns.heatmap(data_2d,annot=arr_2d,fmt="d")# fmt----str="s", decimal="d"
# In[13]:
plt.figure(figsize=(16,9))
annot_kws1={
"fontsize":15,
"fontstyle":"italic",
'color':'k',
"alpha":0.9,
"rotation":"vertical",
"verticalalignment":"center",
"backgroundcolor":"w"
}
sns.heatmap(data1, cmap="coolwarm",annot=True,annot_kws=annot_kws1)
# In[14]:
plt.figure(figsize=(17,10))
sns.heatmap(data1, cmap="coolwarm",annot=True,linewidths=5)
# In[15]:
plt.figure(figsize=(17,10))
sns.heatmap(data1, cmap="coolwarm",annot=True,linewidths=5,linecolor="k")
# In[16]:
plt.figure(figsize=(17,10))
sns.heatmap(data1, cmap="coolwarm",annot=True,linewidths=5,cbar=False)
# In[17]:
plt.figure(figsize=(17,10))
sns.heatmap(data1, cmap="coolwarm",annot=True,linewidths=5,xticklabels=False,yticklabels=False)
# In[18]:
plt.figure(figsize=(17,10))
sns.heatmap(data1, cmap="coolwarm",annot=True,linewidths=5,cbar_kws={
#"orientation":"vertical",
"orientation":"horizontal",
"shrink":1,
"extend":"min",#min,max,both
"extendfrac":0.1,
"ticks":np.arange(0,22),
"drawedges":True,
})
# In[19]:
plt.figure(figsize=(17,10))
sns.heatmap(data1,annot=True,linewidths=5,cbar_kws={
"orientation":"vertical",
# "orientation":"horizontal",
"shrink":1,
"extend":"min", # min,max,both
"extendfrac":0.1,
"ticks":np.arange(0,22),
"drawedges":True,
})
# In[20]:
plt.figure(figsize=(17,10))
# yticklabels= # contry_lab=["a","b","c",............] ap de sakte ho lebale to name
sns.heatmap(data1,annot=True,linewidths=5,xticklabels=np.arange(0,15),yticklabels=np.arange(0,15),cbar_kws={"extend":"min","drawedges":True})
# In[21]:
# # # plt.figure(figsize=(17,10))
# ax=sns.heatmap(data1, cmap="coolwarm",annot=True,linewidths=5)
# ax.set(title="vasim shaikh first heatmap ",
# xlabel="co2 eemmistion ",
# ylabel="Country name")
# sns.set(font_scale=3)
# # Correlation heatmap
# In[22]:
data1.corr()
# In[28]:
plt.figure(figsize=(16,9))
sns.heatmap(data1.corr(),annot=True,linewidth=3)
# In[29]:
plt.figure(figsize=(16,9))
ax=sns.heatmap(data1.corr(),annot=True,linewidth=3)
ax.tick_params(size=10,color="w",labelsize=10,labelcolor="w")
plt.title("heatmap using seaborn", fontsize=15)
plt.show()
# In[30]:
from sklearn.datasets import load_breast_cancer
canncer_dataset=load_breast_cancer()
# In[31]:
canncer_dataset
# In[42]:
canner_df = pd.DataFrame(np.c_[canncer_dataset["data"],canncer_dataset["target"]],
columns=np.append(canncer_dataset["feature_names"],["targget"]))
canner_df
# In[47]:
plt.figure(figsize=(30,30))
ax=sns.heatmap(canner_df.corr(),annot=True,linewidth=3)
ax.tick_params(size=10,color="w",labelsize=10,labelcolor="w")
plt.title("canner_data using seaborn", fontsize=15)
plt.show()
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