Python---数据分析(Pandas十:二维数组DataFrame统计计算一)

1、 count

        用于计算 DataFrame 中非 NaN 值的数量。

DataFrame.count(axis=0, numeric_only=False)

import pandas as pd
import numpy as np

# 创建一个包含 NaN 值的 DataFrame
df = pd.DataFrame({
    'A': [1, 2, np.nan, 4],
    'B': [5, np.nan, np.nan, 8],
    'C': ['foo', 'bar', 'baz', np.nan]
})

print(df)

# 计算每列非 NaN 值的数量
count_per_column = df.count()
print("Count per column:")
print(count_per_column)

# 计算每行非 NaN 值的数量
count_per_row = df.count(axis=1)
print("\nCount per row:")
print(count_per_row)

# 只计算数值列的非 NaN 值的数量
count_numeric_only = df.count(numeric_only=True)
print("\nCount numeric only:")
print(count_numeric_only)

     A    B    C
0  1.0  5.0  foo
1  2.0  NaN  bar
2  NaN  NaN  baz
3  4.0  8.0  NaN
Count per column:
A    3
B    2
C    3
dtype: int64

Count per row:
0    3
1    2
2    1
3    2
dtype: int64

Count numeric only:
A    3
B    2
dtype: int64

2、 sum

        用于计算 DataFrame 中数值的总和。

DataFrame.sum(axis=0, skipna=True, numeric_only=False, min_count=0, **kwargs)

import pandas as pd
import numpy as np

# 创建一个包含 NaN 值的 DataFrame
df = pd.DataFrame({
    'A': [1, 2, np.nan, 4],
    'B': [5, np.nan, np.nan, 8],
    'C': ['foo', 'bar', 'baz', 'qux']
})

print(df)

# 计算每列的总和
sum_per_column = df.sum()
print("Sum per column:")
print(sum_per_column)


# 只计算数值列的总和
sum_numeric_only = df.sum(numeric_only=True)
print("\nSum numeric only:")
print(sum_numeric_only)

# 使用 min_count 参数
sum_with_min_count = df.sum(min_count=3)
print("\nSum with min_count=2:")
print(sum_with_min_count)

     A    B    C
0  1.0  5.0  foo
1  2.0  NaN  bar
2  NaN  NaN  baz
3  4.0  8.0  qux
Sum per column:
A             7.0
B            13.0
C    foobarbazqux
dtype: object

Sum numeric only:
A     7.0
B    13.0
dtype: float64

Sum with min_count=2:
A             7.0
B             NaN
C    foobarbazqux
dtype: object

3、 mean

        用于计算 DataFrame 中数值的平均值。

DataFrame.mean(axis=0, skipna=True, numeric_only=False, **kwargs)

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
})
sum_per_column = df.mean()
print("mean per column:")
print(sum_per_column)

mean per column:
A    2.5
B    6.5
dtype: float64

4、 median

        用于计算 DataFrame 中数值的中位数。

DataFrame.median(axis=0, skipna=True, numeric_only=False, **kwargs)

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
})
sum_per_column = df.median()
print("median per column:")
print(sum_per_column)

mean per column:
A    2.5
B    6.5
dtype: float64

5、 min

        用于计算 DataFrame 中数值的最小值。

DataFrame.min(axis=0, skipna=True, numeric_only=False, **kwargs)

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
})
sum_per_column = df.min()
print("min per column:")
print(sum_per_column)

min per column:
A    1
B    5
dtype: int64

6、 max

        用于计算 DataFrame 中数值的最大值。

DataFrame.max(axis=0, skipna=True, numeric_only=False, **kwargs)

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
})
sum_per_column = df.max()
print("max per column:")
print(sum_per_column)

max per column:
A    4
B    8
dtype: int64

7 、var

        用于计算 DataFrame 中数值的方差。

DataFrame.var(axis=0, skipna=True, ddof=1, numeric_only=False, **kwargs)

import pandas as pd
import numpy as np
df = pd.DataFrame({
    'A': [1, 2, 3, 4],
    'B': [5, 6, 7, 8],
})
sum_per_column = df.var()
print("var per column:")
print(sum_per_column)

var per column:
A    1.666667
B    1.666667
dtype: float64