Upload summarizingdata.py

summarizingdata.py

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+# -*- coding: utf-8 -*-
+"""SummarizingData.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Wo7aUHTjFTRVpiK4efjRHI2gsA6fRip5
+"""
+
+# Import pandas
+import pandas as pd
+
+# Use pandas to read in recent_grads_url
+recent_grads = pd.read_csv("/content/recent_grads.csv")
+
+# Print the shape
+print(recent_grads.shape)
+
+from google.colab import drive
+drive.mount('/content/drive')
+
+# Print .dtypes
+print(recent_grads.dtypes)
+
+# Output summary statistics
+print(recent_grads.describe())
+
+# Exclude data of type object
+print(recent_grads.describe(exclude=["object"]))
+
+# Names of the columns we're searching for missing values
+columns = ['median', 'p25th', 'p75th']
+
+# Take a look at the dtypes
+print(recent_grads[columns].dtypes)
+
+# Find how missing values are represented
+print(recent_grads["median"].unique())
+
+# Replace missing values with NaN
+for column in columns:
+    recent_grads.loc[recent_grads[column] == 'UN', column] = np.nan
+
+import numpy as np
+import pandas as pd
+
+# Assuming 'recent_grads' is your DataFrame and 'columns' is a list of columns needing correction
+
+# Replace missing values with NaN
+for column in columns:
+    recent_grads.loc[recent_grads[column] == 'UN', column] = np.nan
+
+# Select sharewomen column
+sw_col = recent_grads['sharewomen']
+
+# Output first five rows
+print(sw_col.head())
+
+# Import numpy
+import numpy as np
+
+# Use max to output maximum values
+max_sw = recent_grads['sharewomen'].max()
+
+# Print column max
+print(max_sw)
+
+# Output the row containing the maximum percentage of women
+#print(sw_col)
+print(recent_grads[(recent_grads['sharewomen']==max_sw)])
+
+# Convert to numpy array
+import numpy as np
+recent_grads_np=np.array(recent_grads[['unemployed', 'low_wage_jobs']])
+
+
+# Print the type of recent_grads_np
+print(type(recent_grads_np))
+
+print(np.corrcoef(recent_grads_np[:,0], recent_grads_np[:,1]))