Anaconda/Jupyter Notebook/Python 3 KeyError: "Columns not found" - python-3.x

Question

I am trying to make a bar graph with Anaconda/Python 3 with the following code but I keep on getting an error. I first read a dataset that has been formatted to be an excel/csv file and that works fine because I am able to make correlation plots as well as some other plots successfully. I tried the same code with both an excel file and csv file that contain the same data and I get the same error. When the program attempts to execute the line responsible for making a bar graph, I get the following error:
KeyError Traceback (most recent call last)
<ipython-input-4-b72668216a77> in <module>
64
65 # Compare mean and standard deviation between attributes
---> 66 compare = dataset.groupby("target_class")[['mean_profile', 'std_profile', 'kurtosis_profile', 'skewness_profile', 'mean_dmsnr_curve', 'std_dmsnr_curve', 'kurtosis_dmsnr_curve', 'skewness_dmsnr_curve']].mean().reset_index()
67 # compare = dataset.groupby("target_class")[['mean_profile', 'std_profile', 'kurtosis_profile', 'skewness_profile', 'mean_dmsnr_curve', 'std_dmsnr_curve', 'kurtosis_dmsnr_curve', 'skewness_dmsnr_curve']].mean().reset_index()
68
~\Anaconda3\lib\site-packages\pandas\core\base.py in __getitem__(self, key)
263 bad_keys = list(set(key).difference(self.obj.columns))
264 raise KeyError("Columns not found: {missing}"
--> 265 .format(missing=str(bad_keys)[1:-1]))
266 return self._gotitem(list(key), ndim=2)
267
KeyError: "Columns not found: 'mean_dmsnr_curve', 'std_profile', 'std_dmsnr_curve', 'skewness_dmsnr_curve', 'mean_profile', 'kurtosis_profile', 'skewness_profile', 'kurtosis_dmsnr_curve'"
Other plots work fine but the comparison bar graph plot does not. Here is the code that I am trying to execute:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
import itertools
warnings.filterwarnings("ignore")
#matplotlib inline
print("This is a test right before excel file is read")
# Utilize the pandas library to read the data
dataset = pd.read_csv(r'C:\userpath\Machine Learning Project\pulsar_stars_test.csv')
#dataset = pd.read_excel(r'C:\userpath\Machine Learning Project\pulsar_stars_test.xlsx')
# Print the number of rows and columns that the data has to the user
print("This is the number of rows: ", dataset.shape[0])
print("This is the number of columns: ", dataset.shape[1])
# Use pandas to print out the information about the data
print("This is the data information: ", dataset.info())
# Use pandas to display information about missing data
print("This is the missing data: ", dataset.isnull().sum())
# Make a figure appear to display a dataset summary to the user
plt.figure(figsize = (12, 8))
sns.heatmap(dataset.describe()[1:].transpose(), annot = True, linecolor = "w", linewidth = 2, cmap = sns.color_palette("Set2"))
plt.title("Data Summary")
plt.show()
# Instantiate another figure to display some correlation data to the user
correlation = dataset.corr()
plt.figure(figsize = (10, 8))
sns.heatmap(correlation, annot = True, cmap = sns.color_palette("magma"), linewidth = 2, edgecolor = "k")
plt.title("CORRELATION BETWEEN VARIABLES")
plt.show()
# Compute the proportion of each target variabibble in the dataset
plt.figure(figsize = (12, 6))
plt.subplot(121)
ax = sns.countplot(y = dataset["target_class"], palette = ["r", "g"], linewidth = 1, edgecolor = "k"*2)
for i, j in enumerate(dataset["target_class"].value_counts().values):
ax.text(.7, i, j, weight = "bold", fontsize = 27)
plt.title("Count for target variable in dataset")
plt.subplot(122)
plt.pie(dataset["target_class"].value_counts().values, labels = ["not pulsar stars", "pulsar stars"], autopct = "%1.0f%%", wedgeprops = {"linewidth":2, "edgecolor":"white"})
#plt.pie(data["target_class"].value_counts().values, labels = ["not pulsar stars", "pulsar stars"], autopct = "%1.0f%%", wedgeprops = {"linewidth":2, "edgecolor":"white"})
my_circ = plt.Circle((0,0), .7, color = "white")
plt.gca().add_artist(my_circ)
plt.subplots_adjust(wspace = .2)
plt.title("Proportion of target variabibble in dataset")
plt.show()
# Compare mean and standard deviation between attributes
compare = dataset.groupby("target_class")[['mean_profile', 'std_profile', 'kurtosis_profile', 'skewness_profile', 'mean_dmsnr_curve', 'std_dmsnr_curve', 'kurtosis_dmsnr_curve', 'skewness_dmsnr_curve']].mean().reset_index()
# compare = dataset.groupby("target_class")[['mean_profile', 'std_profile', 'kurtosis_profile', 'skewness_profile', 'mean_dmsnr_curve', 'std_dmsnr_curve', 'kurtosis_dmsnr_curve', 'skewness_dmsnr_curve']].mean().reset_index()
compare = compare.drop("target_class", axis = 1)
compare.plot(kind = "bar", width = 0.6, figsize = (13,6), colormap = "Set2")
plt.grid(True, alpha = 0.3)
plt.title("COMPARING MEAN OF ATTRIBUTES FOR TARGET CLASSES")
# Second comparison plot
compare1.dataset.groupby("target_class")[['mean_profile', 'std_profile', 'kurtosis_profile', 'mean_dmsnr_curve', 'std_dmsnr_curve','kurtosis_dmsnr_curve','skewness_dmsnr_curve']].mean().reset_index()
compare1 = compare1.drop("target_class", axis = 1)
compare1.plot(kind = "bar", width = 0.6, figsize = (13, 6), colormap = "Set2")
plt.grid(True, alpha = 0.3)
plt.title("COMPARING STANDARD DEVIATION OF ATTRIBUTES FOR TARGET CLASSES")
plt.show()
The program fails when it hits the line (line 66) at which the groupby function is called for the first time in the program. Does anybody know how to fix this?

Answer 1

Plz update your version of pandas ie package whatever u r using....plz update that...then and then key error will be solve