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
探索数据
将数据导入 Pandas 数据帧后，可以使用这些方法来了解数据的外观：
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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
选择
通常，您可能需要选择单个元素或数据的某个子集来检查它或执行进一步分析。这些方法会派上用场：
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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
数据清理
如果您正在使用真实世界的数据，您可能需要清理它。这些是一些有用的方法：
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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
过滤，排序和分组
过滤，排序和分组数据的方法：
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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
加入和组合
组合两个数据帧的方法：
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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
写数据
最后，当您通过分析生成结果时，有几种方法可以导出数据：
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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
