Python has powerful data analysis and processing capabilities. To use Python for data analysis, you need to master the three Python packages: pandas, matplotlib, and seaborn. Mastering the knowledge of Python data analysis can help us better discover data. The laws and trends behind it provide support for business decisions.
Reading data using Pandas
First, import the pandas library. The pandas library has powerful data processing capabilities. Use the read_excel function to import data. Just import the file path to import the data. Head can preview the first 5 rows of data.
import pandas as pddf=pd.read_excel(r'C:\Desktop\e-commerce sales data-August 23.xlsx')df.head()#Preview the first 5 rows of data
The info function can look at the information of each field, including non-null value count and data type. For example, the date here is date data, the area is character data, and the customer’s age is numeric data.
df.info()#Data information preview
Use the describe function to perform descriptive statistics on the data. The descriptive statistics include counts, averages, standard deviations, minimum values, maximum values, etc. For example, if you perform descriptive statistics on customer age, the average customer age is 42 years old, and the youngest customer is 22 years old. , the oldest customer is 62 years old.
df.describe()#Data description statistics
Using Pandas data sorting
The sort_values function can be used to sort data by importing the sorted data column. The default sorting is ascending order.
df.sort_values(by='sales',inplace=True)#Default sort df in ascending order
The ascending=False parameter can be sorted in descending order.
df.sort_values(by='sales',inplace=True,ascending=False)#Sort df in descending order
If you want to customize the sorting, such as ascending order by product category, descending order by sales volume, by importing custom data columns, ascending setting parameters, True ascending order, False descending order.
df.sort_values(by=['Product Category','Sales'], ascending=[True,False],inplace=True)#Customized sorting df
Use Pandas data filtering
Data filtering uses data frame [] to filter. Write conditions in the data frame for data filtering. & Filter when all conditions are met. == Filter when specific conditions are met. This means filtering data where the customer’s gender is male and the customer’s age is greater than 60. .
df[(df['Customer gender']=='Male') & amp;(df['Customer age']>60)]#Data filtering & amp;
The following filter area is the data of “Northwest-Gansu Province-Silver” or the product category is “Computer Hardware”, & amp; filters the data when all conditions are met, | filters the data when one of the conditions is met.
df[(df['region']=='Northwest-Gansu Province-Baiyin')|(df['commodity category']=='computer hardware')]#Data filtering|or
isin can filter the data of specific tags. Just write the specific filtering tag within (). For example, the following filters the data of specific order numbers.
df[df['Order number'].isin(['10021296335','10021669688','10021250896','10021434444','10021412817'])]
Data splitting using Pandas
Use the str.split method to split the data in the column, and set the expand parameter to True to return a DataFrame object containing the split data. The region is split into two columns: “province” and “city” as follows.
df_split=df['region'].str.split(pat='-',expand=True)#Data split df['region']=df_split.iloc[:,0]df['province' ]=df_split.iloc[:,1]df['City']=df_split.iloc[:,2]df
Using Pandas statistical operations
Perform statistical operations on the data. Count is used for counting. For example, the order number is counted here. The counting result is 7409 orders.
df['Order Number'].count()#Count
7409
If you want to count non-duplicate product categories, you can first use unique to return a non-duplicate list, and then use len to count the list. The returned product category has 8 unique values.
len(df['Product category'].unique())#unique counting
8
To sum the sales numbers, sum is used for the sum. The result here shows that the total number of sales is 48354.
df['Sales'].sum()#Sum
48354
To count the number of orders for each product category, use groupby to count the orders to get the number of orders for each product category.
df.groupby(['Product category'])['Order number'].count().reset_index()#Group count
Using Python data graphing
The matplotlib and seaborn libraries in Python have powerful data visualization functions. They can count the sales in each region, import the matplotlib package, pass in the sales data column, and calculate specific sales data. After setting the chart parameters, it can be concluded that the sales volume in the South China region accounts for a maximum of 36.3%, and the sales volume in the Southwest region accounts for a minimum of 3.1%.
import matplotlib.pyplot as plt import matplotlib.style as pslplt.rcParams['font.sans-serif']=['SimHei'] #Used to display Chinese labels normally plt.rcParams['axes.unicode_minus']= False #Used to display negative signs normally psl.use('ggplot')df_QY=df.groupby(['Area'])['Sales'].count().reset_index()#Pie chart labels = df_QY[' Area'].tolist()explode = [0.05,0.05,0,0,0,0] # Used to highlight data df_QY['Sales Number'].plot(kind='pie',figsize=(9,6 ), autopct='%.1f%%', #data labels labels=labels, startangle=260, #Initial angle explode=explode, # Highlight data pctdistance=0.87, # Set the distance between the percentage label and the center of the circle textprops = {' fontsize':12, 'color':'k'}, #Set the attribute value of the text label)plt.title("Sales proportion of each region")plt.show()
Make a boxplot of profits, use the boxplot function, and set the parameters of the boxplot chart to get the data distribution of profits. Most of the profit data in the boxplot exceeds the upper and lower limits of the boxplot.
import matplotlib.pyplot as plt import matplotlib.style as pslplt.rcParams['font.sans-serif']=['SimHei'] #Used to display Chinese labels normally plt.rcParams['axes.unicode_minus']= False #Used to display negative signs normally psl.use('ggplot')plt.title('Profit Boxplot')df_XB=df[df['Region']=='Northwest']#Boxplot plt.boxplot (x=df_XB['profit'], #Specify the data for drawing the box plot whis=1.5, #Specify 1.5 times the interquartile difference widths=0.1, #Specify the width of the box in the box plot to be 0.3 showmeans=True , #Display mean #patch_artist=True, #The color of the filled box #boxprops={'facecolor':'RoyalBlue'}, #Specify the filling color of the box to be royal blue flierprops={'markerfacecolor':'red','markeredgecolor' :'red','markersize':3}, #Specify the fill color, border color and size of outliers meanprops={'marker':'h','markerfacecolor':'black','markersize':8}, #Specify the marker symbol (dashed line) and color of the median props={'linestyle':'--','color':'orange'}, #Specify the marker symbol (hexagon), fill color and size labels=['Northwest'] )plt.show()
Make a line chart of the sales number and import it into the seaborn library. The date column is used as the X-axis and the sales number is used as the Y-axis. From the line chart, you can see the fluctuation trend of the sales number with the date.
import seaborn as sns import matplotlib.pyplot as plt plt.rcParams['font.sans-serif']=['SimHei'] #Used to display Chinese labels normally plt.rcParams['axes.unicode_minus']=False #Used to display negative signs normally plt.figure(figsize=(10,6)) # Use Seaborn to draw a line chart sns.lineplot(data=df, x='date', y='sales number', color='blue ') # Set the chart title and axis label plt.title('Sales line chart') plt.xlabel('Date') plt.ylabel('Sales') # Display the graph plt.show()
For word cloud analysis of product categories, the wordcloud library can be used to create word cloud charts. Use a dictionary to count the number of product categories. After creating a word cloud object, matplotlib can draw a word cloud chart. From the word cloud chart, it can be seen that bedding sets have the most categories, including office furniture. Furniture has the least variety.
from wordcloud import WordCloud import matplotlib.pyplot as plt # Product category list product_categories = df['Product category'].tolist() # Use dictionary to count the number of product categories category_counts = dict() for category in product_categories: if category in category_counts: category_counts[category] + = 1 else: category_counts[category] = 1 #Create word cloud object wordcloud = WordCloud(font_path='simhei.ttf', background_color='white').generate_from_frequencies(category_counts) #Use matplotlib to draw words Cloud figure plt.figure(figsize=(9, 6)) plt.imshow(wordcloud, interpolation='bilinear') plt.axis("off") plt.show()
Using Python for data analysis requires proficiency in pandas, matplotlib, seaborn and other Python libraries, as well as basic skills in programming and data analysis. If you want to learn more Python data analysis knowledge, you can follow me and continue to share data analysis knowledge to help you better master Python!
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