Indexing issue after Transposing dataframe
Question:
I have the following code below, to produce the heatmap in the image. However, as there are many ‘Indicators’ – I would like the heat map to be long horizontal and not tall. I.e., the Indicators on the X axis, and the Criteria (robustness…etc) along the left side of the y axis.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
df = pd.read_csv('Res_Gov.csv')
df1 = df.transpose()
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(12, 12))
colors = ["#f4a261","#2a9d8f"]
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))
# Draw the heatmap with the mask and correct aspect ratio
df1 = sns.heatmap(df1.set_index('Indicators'), cmap=cmap, square=True, linewidths=.5, cbar_kws={"shrink": .5})
# Set the colorbar labels
colorbar = ax.collections[0].colorbar
colorbar.set_ticks([0.25,0.75])
colorbar.set_ticklabels(['Not Applicable', 'Applicable'])
Without the transpose function, I receive this:
But, when I use it, I receive just a blank square graph.
I know there is an indexing issue happing but am not sure what it is. Any help would be appreciated! Thank you!
Df snippet – before transpose
,Indicators,Robustness,Flexibility,Resourcefulness,Redundancy,Diversity,Independence,Foresight Capacity,Coordination Capacitiy,Collaboration Capacity,Connectivity & Interdependence,Agility,Adaptability,Self-Organization,Creativity & Innovation,Efficiency,Equity
0,G1,1,1,1,0,0,1,1,1,1,1,1,1,0,1,1,1
1,G2,1,0,0,0,0,1,0,1,1,1,1,0,0,1,1,1
2,G3,1,0,1,0,0,1,1,1,1,1,1,1,1,1,0,1
3,G4,1,1,1,0,1,0,1,1,1,1,1,1,1,1,0,1
4,G5,1,0,1,0,0,0,0,1,0,1,1,0,0,0,1,0
5,G6,1,0,1,0,1,0,1,0,0,0,0,0,0,1,0,1
6,G7,1,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0
7,G8,1,1,0,0,0,1,1,1,1,0,1,1,0,0,0,0
8,G9,1,0,1,0,0,1,1,1,1,0,1,1,0,1,0,1
9,G10,1,1,1,0,0,0,1,1,1,1,1,0,0,0,1,1
10,G11,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1
11,G12,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,0
12,G13,1,1,1,0,1,0,1,1,0,1,1,0,0,0,0,0
13,G14,1,0,1,0,1,0,1,1,1,1,1,1,0,0,1,1
14,G15,1,1,1,0,1,0,1,1,1,1,1,1,1,1,0,1
15,G16,1,0,1,0,1,1,0,1,1,1,0,1,1,1,0,1
16,G17,1,1,1,0,0,0,0,0,0,0,1,1,0,1,1,0
17,G18,1,0,1,0,1,1,1,1,1,1,0,1,1,1,0,1
18,G19,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
19,G20,1,1,0,1,1,0,0,0,1,0,0,0,1,0,0,1
20,G21,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1
21,G22,1,1,1,0,0,0,1,1,1,1,1,1,0,1,0,1
22,G23,1,0,1,0,0,1,1,0,1,0,0,1,1,1,0,0
Answers:
You need set Indicators to index before tranpose and then remove from sns.heatmap:
#create index by first column (from sample data, in real should be removed `index_col`)
df = pd.read_csv('Res_Gov.csv', index_col=0)
#create index before transpose
df1 = df.set_index('Indicators').T
Or:
df = pd.read_csv('Res_Gov.csv', index_col='Indicators')
#create index before transpose
df1 = df.T
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(12, 12))
colors = ["#f4a261","#2a9d8f"]
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))
# Draw the heatmap with the mask and correct aspect ratio
#remove create index
df1 = sns.heatmap(df1, cmap=cmap, square=True, linewidths=.5, cbar_kws={"shrink": .5})
# Set the colorbar labels
colorbar = ax.collections[0].colorbar
colorbar.set_ticks([0.25,0.75])
colorbar.set_ticklabels(['Not Applicable', 'Applicable'])
I made some minor modifications:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
df = pd.read_csv('Res_Gov.csv', index_col=0) # HERE
df1 = df.set_index('Indicators').T # HERE
# Set up the matplotlib figure
fig, ax = plt.subplots(figsize=(12, 12))
colors = ["#f4a261","#2a9d8f"]
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))
# Draw the heatmap with the mask and correct aspect ratio
df1 = sns.heatmap(df1, cmap=cmap, square=True,
linewidths=.5, cbar_kws={"shrink": .5}) # HERE
# Set the colorbar labels
colorbar = ax.collections[0].colorbar
colorbar.set_ticks([0.25,0.75])
colorbar.set_ticklabels(['Not Applicable', 'Applicable'])
fig.tight_layout() # HERE
plt.show()
Note: if you have not the first unnamed column with 0, 1, 2, 3, …, you can remove index_col=0 from pd.read_csv
I have the following code below, to produce the heatmap in the image. However, as there are many ‘Indicators’ – I would like the heat map to be long horizontal and not tall. I.e., the Indicators on the X axis, and the Criteria (robustness…etc) along the left side of the y axis.
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
df = pd.read_csv('Res_Gov.csv')
df1 = df.transpose()
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(12, 12))
colors = ["#f4a261","#2a9d8f"]
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))
# Draw the heatmap with the mask and correct aspect ratio
df1 = sns.heatmap(df1.set_index('Indicators'), cmap=cmap, square=True, linewidths=.5, cbar_kws={"shrink": .5})
# Set the colorbar labels
colorbar = ax.collections[0].colorbar
colorbar.set_ticks([0.25,0.75])
colorbar.set_ticklabels(['Not Applicable', 'Applicable'])
Without the transpose function, I receive this:
But, when I use it, I receive just a blank square graph.
I know there is an indexing issue happing but am not sure what it is. Any help would be appreciated! Thank you!
Df snippet – before transpose
,Indicators,Robustness,Flexibility,Resourcefulness,Redundancy,Diversity,Independence,Foresight Capacity,Coordination Capacitiy,Collaboration Capacity,Connectivity & Interdependence,Agility,Adaptability,Self-Organization,Creativity & Innovation,Efficiency,Equity
0,G1,1,1,1,0,0,1,1,1,1,1,1,1,0,1,1,1
1,G2,1,0,0,0,0,1,0,1,1,1,1,0,0,1,1,1
2,G3,1,0,1,0,0,1,1,1,1,1,1,1,1,1,0,1
3,G4,1,1,1,0,1,0,1,1,1,1,1,1,1,1,0,1
4,G5,1,0,1,0,0,0,0,1,0,1,1,0,0,0,1,0
5,G6,1,0,1,0,1,0,1,0,0,0,0,0,0,1,0,1
6,G7,1,1,0,1,0,1,0,0,0,0,1,0,0,0,0,0
7,G8,1,1,0,0,0,1,1,1,1,0,1,1,0,0,0,0
8,G9,1,0,1,0,0,1,1,1,1,0,1,1,0,1,0,1
9,G10,1,1,1,0,0,0,1,1,1,1,1,0,0,0,1,1
10,G11,1,0,1,0,0,0,1,0,0,0,1,0,0,0,0,1
11,G12,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,0
12,G13,1,1,1,0,1,0,1,1,0,1,1,0,0,0,0,0
13,G14,1,0,1,0,1,0,1,1,1,1,1,1,0,0,1,1
14,G15,1,1,1,0,1,0,1,1,1,1,1,1,1,1,0,1
15,G16,1,0,1,0,1,1,0,1,1,1,0,1,1,1,0,1
16,G17,1,1,1,0,0,0,0,0,0,0,1,1,0,1,1,0
17,G18,1,0,1,0,1,1,1,1,1,1,0,1,1,1,0,1
18,G19,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
19,G20,1,1,0,1,1,0,0,0,1,0,0,0,1,0,0,1
20,G21,1,1,1,0,0,0,0,1,1,1,1,0,0,0,0,1
21,G22,1,1,1,0,0,0,1,1,1,1,1,1,0,1,0,1
22,G23,1,0,1,0,0,1,1,0,1,0,0,1,1,1,0,0
You need set Indicators to index before tranpose and then remove from sns.heatmap:
#create index by first column (from sample data, in real should be removed `index_col`)
df = pd.read_csv('Res_Gov.csv', index_col=0)
#create index before transpose
df1 = df.set_index('Indicators').T
Or:
df = pd.read_csv('Res_Gov.csv', index_col='Indicators')
#create index before transpose
df1 = df.T
# Set up the matplotlib figure
f, ax = plt.subplots(figsize=(12, 12))
colors = ["#f4a261","#2a9d8f"]
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))
# Draw the heatmap with the mask and correct aspect ratio
#remove create index
df1 = sns.heatmap(df1, cmap=cmap, square=True, linewidths=.5, cbar_kws={"shrink": .5})
# Set the colorbar labels
colorbar = ax.collections[0].colorbar
colorbar.set_ticks([0.25,0.75])
colorbar.set_ticklabels(['Not Applicable', 'Applicable'])
I made some minor modifications:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
df = pd.read_csv('Res_Gov.csv', index_col=0) # HERE
df1 = df.set_index('Indicators').T # HERE
# Set up the matplotlib figure
fig, ax = plt.subplots(figsize=(12, 12))
colors = ["#f4a261","#2a9d8f"]
cmap = LinearSegmentedColormap.from_list('Custom', colors, len(colors))
# Draw the heatmap with the mask and correct aspect ratio
df1 = sns.heatmap(df1, cmap=cmap, square=True,
linewidths=.5, cbar_kws={"shrink": .5}) # HERE
# Set the colorbar labels
colorbar = ax.collections[0].colorbar
colorbar.set_ticks([0.25,0.75])
colorbar.set_ticklabels(['Not Applicable', 'Applicable'])
fig.tight_layout() # HERE
plt.show()
Note: if you have not the first unnamed column with 0, 1, 2, 3, …, you can remove index_col=0 from pd.read_csv