• 引言
• 結尾
• 參考文章

引言

Pandas，Numpy和Scikit-Learn是最受歡迎的Python資料科學和分析庫。

Numpy用於較低級別的科學計算。Pandas構建於Numpy之上，專為Python中的實際資料分析而設計。以下是我找的一個由 Kara Tan 大牛提供的一張關於Pandas最常見和最有用的功能的備忘單，我們直接跳吧！

備忘單（讓我們起飛）

匯入資料

任何型別的資料分析都從獲取某些資料開始。Pandas為您提供了很多將資料匯入Python工作簿的選項：

pd.read_csv(filename) # From a CSV file
pd.read_table(filename) # From a delimited text file (like TSV)
 

pd.read_excel(filename) # From an Excel file
pd.read_sql(query, connection_object) # Reads from a SQL table/database
pd.read_json(json_string) # Reads from a JSON formatted string, URL or file.
pd.read_html(url) # Parses an html URL, string or file and extracts tables to a list of dataframes
pd.read_clipboard() # Takes the contents of your clipboard and passes it to read_table()
 

pd.DataFrame(dict) # From a dict, keys for columns names, values for data as lists

探索資料

將資料匯入Pandas資料幀後，可以使用這些方法來了解資料的外觀：

df.shape() # Prints number of rows and columns in dataframe
df.head(n) # Prints first n rows of the DataFrame
df.tail(n) # Prints last n rows of the DataFrame
df.info() # Index, Datatype and Memory information
 

df.describe() # Summary statistics for numerical columns
s.value_counts(dropna=False) # Views unique values and counts
df.apply(pd.Series.value_counts) # Unique values and counts for all columns
df.describe() # Summary statistics for numerical columns
df.mean() # Returns the mean of all columns
df.corr() # Returns the correlation between columns in a DataFrame
df.count() # Returns the number of non-null values in each DataFrame column
df.max() # Returns the highest value in each column
df.min() # Returns the lowest value in each column
df.median() # Returns the median of each column
df.std() # Returns the standard deviation of each column

選擇

通常，您可能需要選擇單個元素或資料的某個子集來檢查它或執行進一步分析。這些方法會派上用場：

df[col] # Returns column with label col as Series
df[[col1, col2]] # Returns Columns as a new DataFrame
s.iloc[0] # Selection by position (selects first element)
s.loc[0] # Selection by index (selects element at index 0)
df.iloc[0,:] # First row
df.iloc[0,0] # First element of first column

資料清理

如果您正在使用真實世界的資料，您可能需要清理它。這些是一些有用的方法：

df.columns = ['a','b','c'] # Renames columns
pd.isnull() # Checks for null Values, Returns Boolean Array
pd.notnull() # Opposite of s.isnull()
df.dropna() # Drops all rows that contain null values
df.dropna(axis=1) # Drops all columns that contain null values
df.dropna(axis=1,thresh=n) # Drops all rows have have less than n non null values
df.fillna(x) # Replaces all null values with x
s.fillna(s.mean()) # Replaces all null values with the mean (mean can be replaced with almost any function from the statistics section)
s.astype(float) # Converts the datatype of the series to float
s.replace(1,'one') # Replaces all values equal to 1 with 'one'
s.replace([1,3],['one','three']) # Replaces all 1 with 'one' and 3 with 'three'
df.rename(columns=lambda x: x + 1) # Mass renaming of columns
df.rename(columns={'old_name': 'new_ name'}) # Selective renaming
df.set_index('column_one') # Changes the index
df.rename(index=lambda x: x + 1) # Mass renaming of index

過濾，排序和分組

過濾，排序和分組資料的方法：

df[df[col] > 0.5] # Rows where the col column is greater than 0.5
df[(df[col] > 0.5) & (df[col] < 0.7)] # Rows where 0.5 < col < 0.7
df.sort_values(col1) # Sorts values by col1 in ascending order
df.sort_values(col2,ascending=False) # Sorts values by col2 in descending order
df.sort_values([col1,col2], ascending=[True,False]) # Sorts values by col1 in ascending order then col2 in descending order
df.groupby(col) # Returns a groupby object for values from one column
df.groupby([col1,col2]) # Returns a groupby object values from multiple columns
df.groupby(col1)[col2].mean() # Returns the mean of the values in col2, grouped by the values in col1 (mean can be replaced with almost any function from the statistics section)
df.pivot_table(index=col1, values= col2,col3], aggfunc=mean) # Creates a pivot table that groups by col1 and calculates the mean of col2 and col3
df.groupby(col1).agg(np.mean) # Finds the average across all columns for every unique column 1 group
df.apply(np.mean) # Applies a function across each column
df.apply(np.max, axis=1) # Applies a function across each row

加入和組合

組合兩個資料幀的方法：

df1.append(df2) # Adds the rows in df1 to the end of df2 (columns should be identical)
pd.concat([df1, df2],axis=1) # Adds the columns in df1 to the end of df2 (rows should be identical)
df1.join(df2,on=col1,how='inner') # SQL-style joins the columns in df1 with the columns on df2 where 

寫資料

最後，當您通過分析生成結果時，有幾種方法可以匯出資料：

df.to_csv(filename) # Writes to a CSV file
df.to_excel(filename) # Writes to an Excel file
df.to_sql(table_name, connection_object) # Writes to a SQL table
df.to_json(filename) # Writes to a file in JSON format
df.to_html(filename) # Saves as an HTML table
df.to_clipboard() # Writes to the clipboard

結尾

雖然我學過Pandas，還寫了Pandas學習筆記（5篇），但是到用的時候很容易忘記我需要的功能怎麼實現，怎麼寫程式碼。這張備忘單讓我收益匪淺！

參考文章