Pandas是入门Python做数据分析必须要掌握的一个库,是一个开放源码、BSD 许可的库,提供高性能、易于使用的数据结构和数据分析的工具。主要数据结构是 Series (一维数据)与 DataFrame(二维数据),这两种数据结构足以处理金融、统计、社会科学、工程等领域里的大多数典型用例。今天就来一起学习。
# 运行以下代码 import pandas as pd import datetime
import pandas as pd
# 运行以下代码 path6 = "../input/pandas_exercise/pandas_exercise/exercise_data/wind.data" # wind.data
import datetime
# 运行以下代码 data = pd.read_table(path6, sep = "\s+", parse_dates = [[0,1,2]]) data.head()
# 运行以下代码 def fix_century(x): year = x.year - 100 if x.year > 1989 else x.year return datetime.date(year, x.month, x.day) # apply the function fix_century on the column and replace the values to the right ones data['Yr_Mo_Dy'] = data['Yr_Mo_Dy'].apply(fix_century) # data.info() data.head()
# 运行以下代码 # transform Yr_Mo_Dy it to date type datetime64 data["Yr_Mo_Dy"] = pd.to_datetime(data["Yr_Mo_Dy"]) # set 'Yr_Mo_Dy' as the index data = data.set_index('Yr_Mo_Dy') data.head() # data.info()
# 运行以下代码 data.isnull().sum()
# 运行以下代码 data.shape[0] - data.isnull().sum()
# 运行以下代码 data.mean().mean()
10.227982360836924
# 运行以下代码 loc_stats = pd.DataFrame() loc_stats['min'] = data.min() # min loc_stats['max'] = data.max() # max loc_stats['mean'] = data.mean() # mean loc_stats['std'] = data.std() # standard deviations loc_stats
# 运行以下代码 # create the dataframe day_stats = pd.DataFrame() # this time we determine axis equals to one so it gets each row. day_stats['min'] = data.min(axis = 1) # min day_stats['max'] = data.max(axis = 1) # max day_stats['mean'] = data.mean(axis = 1) # mean day_stats['std'] = data.std(axis = 1) # standard deviations day_stats.head()
注意:1961年的1月和1962年的1月应该区别对待
# 运行以下代码 # creates a new column 'date' and gets the values from the index data['date'] = data.index # creates a column for each value from date data['month'] = data['date'].apply(lambda date: date.month) data['year'] = data['date'].apply(lambda date: date.year) data['day'] = data['date'].apply(lambda date: date.day) # gets all value from the month 1 and assign to janyary_winds january_winds = data.query('month == 1') # gets the mean from january_winds, using .loc to not print the mean of month, year and day january_winds.loc[:,'RPT':"MAL"].mean()
# 运行以下代码 data.query('month == 1 and day == 1')
# 运行以下代码 data.query('day == 1')
# 运行以下代码 import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import numpy as np %matplotlib inline
# 运行以下代码 path7 = '../input/pandas_exercise/pandas_exercise/exercise_data/train.csv' # train.csv
# 运行以下代码 titanic = pd.read_csv(path7) titanic.head()
# 运行以下代码 titanic.set_index('PassengerId').head()
# 运行以下代码 # sum the instances of males and females males = (titanic['Sex'] == 'male').sum() females = (titanic['Sex'] == 'female').sum() # put them into a list called proportions proportions = [males, females] # Create a pie chart plt.pie( # using proportions proportions, # with the labels being officer names labels = ['Males', 'Females'], # with no shadows shadow = False, # with colors colors = ['blue','red'], # with one slide exploded out explode = (0.15 , 0), # with the start angle at 90% startangle = 90, # with the percent listed as a fraction autopct = '%1.1f%%' ) # View the plot drop above plt.axis('equal') # Set labels plt.title("Sex Proportion") # View the plot plt.tight_layout() plt.show()
# 运行以下代码 # creates the plot using lm = sns.lmplot(x = 'Age', y = 'Fare', data = titanic, hue = 'Sex', fit_reg=False) # set title lm.set(title = 'Fare x Age') # get the axes object and tweak it axes = lm.axes axes[0,0].set_ylim(-5,) axes[0,0].set_xlim(-5,85) (-5, 85)
# 运行以下代码 titanic.Survived.sum()
342
# 运行以下代码 # sort the values from the top to the least value and slice the first 5 items df = titanic.Fare.sort_values(ascending = False) df # create bins interval using numpy binsVal = np.arange(0,600,10) binsVal # create the plot plt.hist(df, bins = binsVal) # Set the title and labels plt.xlabel('Fare') plt.ylabel('Frequency') plt.title('Fare Payed Histrogram') # show the plot plt.show()
# 运行以下代码 import pandas as pd
# 运行以下代码 raw_data = {"name": ['Bulbasaur', 'Charmander','Squirtle','Caterpie'], "evolution": ['Ivysaur','Charmeleon','Wartortle','Metapod'], "type": ['grass', 'fire', 'water', 'bug'], "hp": [45, 39, 44, 45], "pokedex": ['yes', 'no','yes','no'] }
# 运行以下代码 pokemon = pd.DataFrame(raw_data) pokemon.head()
# 运行以下代码 pokemon = pokemon[['name', 'type', 'hp', 'evolution','pokedex']] pokemon
# 运行以下代码 pokemon['place'] = ['park','street','lake','forest'] pokemon
# 运行以下代码 pokemon.dtypes
# 运行以下代码 import pandas as pd import numpy as np # visualization import matplotlib.pyplot as plt %matplotlib inline
# 运行以下代码 path9 = '../input/pandas_exercise/pandas_exercise/exercise_data/Apple_stock.csv' # Apple_stock.csv
# 运行以下代码 apple = pd.read_csv(path9) apple.head()
# 运行以下代码 apple.dtypes
# 运行以下代码 apple.Date = pd.to_datetime(apple.Date) apple['Date'].head()
# 运行以下代码 apple = apple.set_index('Date') apple.head()
# 运行以下代码 apple.index.is_unique
True
# 运行以下代码 apple.sort_index(ascending = True).head()
# 运行以下代码 apple_month = apple.resample('BM') apple_month.head()
# 运行以下代码 (apple.index.max() - apple.index.min()).days
12261
# 运行以下代码 apple_months = apple.resample('BM').mean() len(apple_months.index)
404
# 运行以下代码 # makes the plot and assign it to a variable appl_open = apple['Adj Close'].plot(title = "Apple Stock") # changes the size of the graph fig = appl_open.get_figure() fig.set_size_inches(13.5, 9)
# 运行以下代码 import pandas as pd
# 运行以下代码 path10 ='../input/pandas_exercise/pandas_exercise/exercise_data/iris.csv' # iris.csv
# 运行以下代码 iris = pd.read_csv(path10) iris.head()
iris = pd.read_csv(path10,names = ['sepal_length','sepal_width', 'petal_length', 'petal_width', 'class']) iris.head()
# 运行以下代码 pd.isnull(iris).sum()
# 运行以下代码 iris.iloc[10:20,2:3] = np.nan iris.head(20)
# 运行以下代码 iris.petal_length.fillna(1, inplace = True) iris
# 运行以下代码 del iris['class'] iris.head()
# 运行以下代码 iris.iloc[0:3 ,:] = np.nan iris.head()
# 运行以下代码 iris = iris.dropna(how='any') iris.head()
# 运行以下代码 iris = iris.reset_index(drop = True) iris.head()
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