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Author: joeyajames

Pandas/pandas_weather.py

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+import numpy as np
+import pandas as pd
+
+def header(msg):
+	print('-' * 50)
+	print('[ ' + msg + ' ]')
+
+# 1. load hard-coded data into a dataframe
+header("1. load hard-coded data into a df")
+df = pd.DataFrame(
+	[['Jan',58,42,74,22,2.95],
+	['Feb',61,45,78,26,3.02],
+	['Mar',65,48,84,25,2.34],
+	['Apr',67,50,92,28,1.02],
+	['May',71,53,98,35,0.48],
+	['Jun',75,56,107,41,0.11],
+	['Jul',77,58,105,44,0.0],
+	['Aug',77,59,102,43,0.03],
+	['Sep',77,57,103,40,0.17],
+	['Oct',73,54,96,34,0.81],
+	['Nov',64,48,84,30,1.7],
+	['Dec',58,42,73,21,2.56]],
+	index = [0,1,2,3,4,5,6,7,8,9,10,11],
+	columns = ['month','avg_high','avg_low','record_high','record_low','avg_precipitation'])
+print(df)
+
+# 2. read text file into a dataframe
+header("2. read text file into a df")
+filename = 'Fremont_weather.txt'
+df = pd.read_csv(filename)
+print(df)
+
+# 3. print first 5 or last 3 rows of df
+header("3. df.head()")
+print(df.head())
+header("3. df.tail(3)")
+print(df.tail(3))
+
+# 4. get data types, index, columns, values
+header("4. df.dtypes")
+print(df.dtypes)
+
+header("4. df.index")
+print(df.index)
+
+header("4. df.columns")
+print(df.columns)
+
+header("4. df.values")
+print(df.values)
+
+# 5. statistical summary of each column
+header("5. df.describe()")
+print(df.describe())
+
+# 6. sort records by any column
+header("6. df.sort_values('record_high', ascending=False)")
+print (df.sort_values('record_high', ascending=False))
+
+# 7. slicing records
+header("7. slicing -- df.avg_low")
+print(df.avg_low)							# index with single column
+
+header("7. slicing -- df['avg_low']")
+print(df['avg_low'])
+
+header("7. slicing -- df[2:4]")				# index with single column
+print(df[2:4])								# rows 2 to 3
+
+header("7. slicing -- df[['avg_low','avg_high']]")
+print(df[['avg_low','avg_high']])
+
+header("7. slicing -- df.loc[:,['avg_low','avg_high']]")
+print(df.loc[:,['avg_low','avg_high']])		# multiple columns: df.loc[from_row:to_row,['column1','column2']]
+
+header("7. slicing scalar value -- df.loc[9,['avg_precipitation']]")
+print(df.loc[9,['avg_precipitation']])
+
+header("7. df.iloc[3:5,[0,3]]")				# index location can receive range or list of indices
+print(df.iloc[3:5,[0,3]])
+
+# 8. filtering
+header("8. df[df.avg_precipitation > 1.0]")	# filter on column values
+print(df[df.avg_precipitation > 1.0])
+
+header("8. df[df['month'].isin['Jun','Jul','Aug']]")
+print(df[df['month'].isin(['Jun','Jul','Aug'])])
+
+# 9. assignment -- very similar to slicing
+header("9. df.loc[9,['avg_precipitation']] = 101.3")
+df.loc[9,['avg_precipitation']] = 101.3
+print(df.iloc[9:11])
+
+header("9. df.loc[9,['avg_precipitation']] = np.nan")
+df.loc[9,['avg_precipitation']] = np.nan
+print(df.iloc[9:11])
+
+header("9. df.loc[:,'avg_low'] = np.array([5] * len(df))")
+df.loc[:,'avg_low'] = np.array([5] * len(df))
+print(df.head())
+
+header("9. df['avg_day'] = (df.avg_low + df.avg_high) / 2")
+df['avg_day'] = (df.avg_low + df.avg_high) / 2
+print(df.head())
+
+# 10. renaming columns
+header("10. df.rename(columns = {'avg_precipitation':'avg_rain'}, inplace=True)")
+df.rename(columns = {'avg_precipitation':'avg_rain'}, inplace=True)		# rename 1 column
+print(df.head())
+
+header("10. df.columns = ['month','av_hi','av_lo','rec_hi','rec_lo','av_rain','av_day']")
+df.columns = ['month','av_hi','av_lo','rec_hi','rec_lo','av_rain','av_day']
+print(df.head())
+
+# 11. iterate a df
+header("11. iterate rows of df with a for loop")
+for index, row in df.iterrows():
+    print (index, row["month"], row["avg_high"])
+
+# 12. write to csv file
+df.to_csv('foo.csv')
+
+